Publications
2026
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Human-in-the-loop Reinforcement Learning for personalized and adaptive interventions in wellness and healthcare: A systematic literature review
CONCLUSIONS: RL offers an opportunity to personalize treatments and optimize clinical decision-making processes employing sequential data. However, the lack of shared methodological standards, transparency in model descriptions, and limited number of prospective studies constitute significant barriers to its adoption. In conclusion, better documented datasets, comparable metrics, and prospective evaluations are required to establish clinical effectiveness and support future implementation. -
Novel Sensors and Data-Driven Approach to Support Prevention, Diagnosis, and Treatment Planning of Cerebrovascular Accidents: Protocol for a Multimodal Data Collection Study With Novel Sensors
CONCLUSIONS: We successfully conducted a multicenter multimodal data collection study on healthy controls and patients with stroke and TIA. This protocol paper provides a comprehensive methodological description of the Stroke-data study, serving as a reference for previously published and future analyses based on the dataset. The protocol is unique in combining physiological sensor data, video recordings, clinical assessments, questionnaires, and health record data from patients with stroke and... -
Prediction of in-hospital atrial fibrillation after acute myocardial infarction: a cross-cohort study in Italian and Finnish patients
CONCLUSIONS: Our study demonstrates that machine learning models using routinely collected clinical parameters could help predict in-hospital AF in post-AMI patients across different populations and national healthcare systems. Notably, a simple and fully interpretable logistic regression model achieved robust performance in the first cross-national validation for this prediction task, suggesting that more complex, black-box approaches may offer limited additional benefit for this specific task.... -
Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration
CONCLUSIONS: This scoping review maps xAI implementation across multiple cancer imaging modalities, revealing methodological inconsistency and insufficient validation. Most research emphasizes visualization over quantitative interpretability, and few models are reproducible or clinically implemented. These findings provide an evidence base for researchers, clinicians, and regulators to prioritize standardization of xAI reporting, quantitative validation, and user-centered frameworks to advance... -
Blood biomarker outliers and clinical events during intensive care in patients with traumatic brain injury
INTRODUCTION: Monitoring and timely clinical assessment are fundamental to treating patients with traumatic brain injury (TBI) in the intensive care unit (ICU). Proactive, individualised treatment is unavailable with the current methods. -
Machine learning in critical care: Improving prediction of mortality and intensive care unit stay after cardiac surgery
CONCLUSIONS: EuroSCORE II overestimated mortality despite a robust performance. Among tested ML models, the GP classifier demonstrated superior accuracy using preoperative data, further enhanced by perioperative parameters. Our results highlight ML's potential for improving risk assessment by its integration into next-generation cardiac surgery risk assessment tools. -
Performance of the 12-lead ECG in predicting short- and long-term risk of sudden cardiac death
We evaluated the performance of 12-channel ECG in predicting sudden cardiac death across different time intervals using a retrospective data set of 17,625 high-risk cardiac patients who underwent coronary angiography (2007-2018) with follow-up data until 2022. Extreme gradient boosting using 12SL Marquette software-derived parameters from digital ECG recording was used to train and validate models using a random 80/20 split. Model performance was evaluated in both unbalanced and... -
Association of acute blood biomarkers with diffusion tensor imaging and outcome in patients with traumatic brain injury presenting with GCS of 13-15
The aim of the study was to assess the association between blood-based biomarkers of different cellular origins and later white matter integrity, measured using post-acute diffusion tensor metrics, and their relation to outcome in patients presenting with Glasgow Coma Scale of 13-15 after traumatic brain injury (TBI). Admission plasma samples for glial fibrillary acidic protein (GFAP), interleukin 10 (IL-10), heart fatty-acid binding protein (H-FABP), S100 calcium-binding protein B (S100B),... -
Effects of physiotherapy on breathing cycle after thoracic surgery measured with impedance pneumography in a prospective clinical comparison
CONCLUSIONS: IMT seemed to enhance expiration specifically among thoracotomy patients and thus may provide means for enhancing the recovery after thoracic operations. -
Interactive AI annotation of medical images in a virtual reality environment
CONCLUSION: This study is the first to compare the quality of original and interactive AI annotation and to use radiologists' opinions as the measure. More research is needed for generalization. -
Cluster Analysis Reveals Subgroups with Different Risk Profiles and Sickness Absence Patterns in an Occupational Health Cohort
CONCLUSION: Unsupervised and supervised machine learning methods identified six clinically coherent employee clusters, providing information on typical combinations of characteristics and risk profiles of sickness absence.
2025
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Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration
CONCLUSIONS: This scoping review maps xAI implementation across multiple cancer imaging modalities, revealing methodological inconsistency and insufficient validation. Most research emphasizes visualization over quantitative interpretability, and few models are reproducible or clinically implemented. These findings provide an evidence base for researchers, clinicians, and regulators to prioritize standardization of xAI reporting, quantitative validation, and user-centered frameworks to advance... -
Data-Driven Approach to Predict Aneurysm Rupture Risk
No abstract -
Association of acute blood biomarkers with diffusion tensor imaging and outcome in patients with traumatic brain injury presenting with GCS of 13-15
The aim of the study was to assess the association between blood-based biomarkers of different cellular origins and later white matter integrity, measured using post-acute diffusion tensor metrics, and their relation to outcome in patients presenting with Glasgow Coma Scale of 13-15 after traumatic brain injury (TBI). Admission plasma samples for glial fibrillary acidic protein (GFAP), interleukin 10 (IL-10), heart fatty-acid binding protein (H-FABP), S100 calcium-binding protein B (S100B),... -
Enhancing crayfish sex identification with Kolmogorov-Arnold networks and stacked autoencoders
Crayfish play an important role in freshwater ecosystems, and sex classification is crucial for analyzing their demographic structures. This study performed binary classification using traditional machine learning and deep learning models on tabular and image datasets with an imbalanced class distribution. For tabular classification, features related to crayfish weight and size were used. Missing values were handled using different methods to create various datasets. Kolmogorov-Arnold networks... -
Temporal social network modeling of mobile connectivity data with graph neural networks
Graph neural networks (GNNs) have emerged as a state-of-the-art data-driven tool for modeling connectivity data of graph-structured complex networks and integrating information of their nodes and edges in space and time. However, as of yet, the analysis of social networks using the time series of people's mobile connectivity data has not been extensively investigated. In the present study, we investigate four recently proposed snapshot - based temporal GNNs in predicting the phone call and SMS... -
Development of an Explainable-AI Enabled Decision Support System for Improved Risk Assessment of Atrial Fibrillation in Cardiac Patients during Hospital Stay
Cardiovascular disease (CVD) is the primary cause of hospitalization and mortality worldwide, implying a critical burden on the healthcare system. Enhancing CVD risk assessment requires the integration of heterogeneous data sources to provide accurate, robust, and explainable predictions. This study focuses on developing an explainable artificial intelligence decision support system to predict the risk of in-hospital postoperative atrial fibrillation (AF). The use case was selected through... -
Identifying risk factors of long sickness absences: a registry-based study using explainable AI methods
CONCLUSIONS: Other variables besides service use and sickness absence increase the accuracy in predicting long sickness absence and providing information for planning interventions that could have a beneficial impact on work disability risk. -
An AI-powered smart Agribot for detecting locusts in farmlands using IoT and deep learning
In many countries, locusts have significantly harmed agricultural production. To prevent their spread, the Agriculture Robot (Agribot) with cutting-edge technologies like the Internet of Things (IoT) and Machine Learning (ML) can be a possible solution. Thus, this study presents an astute way to develop an Agribot using IoT, ML, and DL-based architecture for detecting locusts in agricultural fields. The IoT framework ensures proper automation by utilizing various agriculture-related sensors, a... -
Music genre classification with modified residual learning and dual neural network
Music Genre is an abstract property of music that can identify shared traditions and conventions. In the recent past, music genre classification has shown a significant role in MIR that has attracted the research community to draw attention all around the world. The subjective aspect of the genre makes it challenging to define, as it relies on listeners' interpretation. Deep Neural architectures can be used to address the efficiency and accuracy issues of traditional music systems. This paper... -
Effects of physiotherapy on breathing cycle after thoracic surgery measured with impedance pneumography in a prospective clinical comparison
CONCLUSIONS: IMT seemed to enhance expiration specifically among thoracotomy patients and thus may provide means for enhancing the recovery after thoracic operations. -
A cluster-assisted differential evolution-based hybrid oversampling method for imbalanced datasets
Class imbalance remains a significant challenge in machine learning, leading to biased models that favor the majority class while failing to accurately classify minority instances. Traditional oversampling methods, such as Synthetic Minority Over-sampling Technique (SMOTE) and its variants, often struggle with class overlap, poor decision boundary representation, and noise accumulation. To address these limitations, this study introduces ClusterDEBO, a novel hybrid oversampling method that... -
Artificial intelligence applications in hypertrophic cardiomyopathy: turns and loopholes
Hypertrophic cardiomyopathy (HCM) is a heterogeneous disease where, despite recent advances, accurate diagnosis, risk stratification, and personalized treatment remain challenging. Artificial intelligence (AI) offers a transformative approach to HCM by enabling rapid, precise analysis of complex data. This article reviews the current and potential applications of AI in HCM. AI enhances diagnostic accuracy by analysing electrocardiograms, echocardiography, and cardiac magnetic resonance images,... -
Interactive AI annotation of medical images in a virtual reality environment
CONCLUSION: This study is the first to compare the quality of original and interactive AI annotation and to use radiologists' opinions as the measure. More research is needed for generalization. -
Interactive 3D segmentation for primary gross tumor volume in oropharyngeal cancer
Radiotherapy is the main treatment modality of oropharyngeal cancer (OPC), in which an accurate segmentation of primary gross tumor volume (GTVt) is essential but also challenging due to significant interobserver variability and the time consumed in manual tumor delineation. For such a challenge an interactive deep learning (DL) based approach offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we investigate an... -
Innovative approaches to collecting, aggregating, and analyzing adverse drug events in smart hospitals
BackgroundThe increasing integration of electronic health records (EHRs) and their secondary use provide new pathways to advance drug safety. Smart hospitals use advanced data collection to enhance pharmacovigilance and better detect adverse drug events (ADEs). Finland's secondary-use legislation embodies this data-sharing shift.ObjectiveThis work synthesizes current evidence and proposes strategies to strengthen ADE detection and analysis in smart hospitals by integrating multimodal data... -
Cluster Analysis Reveals Subgroups with Different Risk Profiles and Sickness Absence Patterns in an Occupational Health Cohort
CONCLUSION: Unsupervised and supervised machine learning methods identified six clinically coherent employee clusters, providing information on typical combinations of characteristics and risk profiles of sickness absence. -
AI-driven wastewater management through comparative analysis of feature selection techniques and predictive models
The integration of artificial intelligence (AI) in wastewater treatment management offers a promising approach to optimizing effluent quality predictions and enhancing operational efficiency. This study evaluates the performance of machine learning models in predicting key wastewater effluent parameters Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Suspended Solids (TSS), Total Effluent Nitrogen and Total Effluent Phosphorus. Three feature selection techniques were... -
A comprehensive evaluation of oversampling techniques for enhancing text classification performance
Class imbalance is a common and critical challenge in text classification tasks, where the underrepresentation of certain classes often impairs the ability of classifiers to learn minority class patterns effectively. According to the "garbage in, garbage out" principle, even high-performing models may fail when trained on skewed distributions. To address this issue, this study investigates the impact of oversampling techniques, specifically the Synthetic Minority Over-sampling Technique (SMOTE)... -
Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and Cascaded DualUNet
Accurate segmentation of the primary gross tumor volumes and metastatic lymph nodes in head and neck cancer is crucial for radiotherapy but remains challenging due to high interobserver variability, highlighting a need for an effective auto-segmentation tool. Tumor delineation is used throughout radiotherapy for treatment planning, initially for pre-radiotherapy (pre-RT) MRI scans followed-up by mid-radiotherapy (mid-RT) during the treatment. For the pre-RT task, we propose a dual-stage 3D UNet... -
Classification of Mycena and <em>Marasmius</em> Species Using Deep Learning Models: An Ecological and Taxonomic Approach
Fungi play a critical role in ecosystems, contributing to biodiversity and providing economic and biotechnological value. In this study, we developed a novel deep learning-based framework for the classification of seven macrofungi species from the genera Mycena and Marasmius, leveraging their unique ecological and morphological characteristics. The proposed approach integrates a custom convolutional neural network (CNN) with a self-organizing map (SOM) adapted for supervised learning and a... -
International multispecialty expert physician preoperative identification of extranodal extension in patients with oropharyngeal cancer using computed tomography: Prospective blinded human inter-observer performance evaluation
CONCLUSIONS: Diagnostic performance for iENE on CT in HPV-positive OPC remains poor across specialties, with high variability and low accuracy. These findings highlight the need for automated systems or improved imaging methods to enhance iENE assessments. -
Blood biomarkers to identify patients with different intracranial lesion combinations after traumatic brain injury
INTRODUCTION: There is a lack of studies examining the most promising blood biomarkers for traumatic brain injury (TBI) in relation to gross pathology types.
2024
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Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration
CONCLUSIONS: This scoping review maps xAI implementation across multiple cancer imaging modalities, revealing methodological inconsistency and insufficient validation. Most research emphasizes visualization over quantitative interpretability, and few models are reproducible or clinically implemented. These findings provide an evidence base for researchers, clinicians, and regulators to prioritize standardization of xAI reporting, quantitative validation, and user-centered frameworks to advance... -
Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and Cascaded DualUNet
Accurate segmentation of the primary gross tumor volumes and metastatic lymph nodes in head and neck cancer is crucial for radiotherapy but remains challenging due to high interobserver variability, highlighting a need for an effective auto-segmentation tool. Tumor delineation is used throughout radiotherapy for treatment planning, initially for pre-radiotherapy (pre-RT) MRI scans followed-up by mid-radiotherapy (mid-RT) during the treatment. For the pre-RT task, we propose a dual-stage 3D UNet... -
Voice Analysis in Dogs with Deep Learning: Development of a Fully Automatic Voice Analysis System for Bioacoustics Studies
Extracting behavioral information from animal sounds has long been a focus of research in bioacoustics, as sound-derived data are crucial for understanding animal behavior and environmental interactions. Traditional methods, which involve manual review of extensive recordings, pose significant challenges. This study proposes an automated system for detecting and classifying animal vocalizations, enhancing efficiency in behavior analysis. The system uses a preprocessing step to segment relevant... -
Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification
This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as Amanita pantherina, Boletus edulis, Cantharellus cibarius, Lactarius deliciosus, Pleurotus ostreatus and Tricholoma terreum were chosen based on their ecological importance and distinct morphological characteristics. The research employed 5 different machine learning techniques and 12 deep learning models, including DenseNet121, MobileNetV2, ConvNeXt,... -
Predicting long-term risk of sudden cardiac death with automatic computer-interpretations of electrocardiogram
CONCLUSION: This proof-of-concept study shows that automatic interpretation of ECG identifies previously validated risk features for SCD. -
Associations of depressive symptoms and psychosocial working conditions with sickness absences in a Finnish cohort of 11,495 employees
CONCLUSIONS: Questionnaires on depressive symptoms and psychosocial work environment can help identify patients with an increased risk of sickness absence. Data accumulating in occupational health care should be used more systematically to reduce the risk of work disability through targeted occupational health interventions. -
Effects of Monitoring Frailty Through a Mobile/Web-Based Application and a Sensor Kit to Prevent Functional Decline in Frail and Prefrail Older Adults: FACET (Frailty Care and Well Function) Pilot Randomized Controlled Trial
CONCLUSIONS: The FACET technological ecosystem helps in the early identification of changes in the functional status of prefrail and frail older adults, facilitating prompt clinical interventions, thereby improving health outcomes in terms of frailty and functional status and potentially preventing disability and dependency. -
Comparative Analysis of Deep Learning Methods on CT Images for Lung Cancer Specification
CONCLUSIONS: The study demonstrates the effectiveness of deep learning models, particularly InceptionResNetV2 and UNet, in both detecting and segmenting lung cancer, showing significant potential for aiding early diagnosis and treatment. Future work could focus on refining these models and exploring their application in other medical domains. -
Psychiatric disorders from EEG signals through deep learning models
Psychiatric disorders present diagnostic challenges due to individuals concealing their genuine emotions, and traditional methods relying on neurophysiological signals have limitations. Our study proposes an improved EEG-based diagnostic model employing Deep Learning (DL) techniques to address this. By experimenting with DL models on EEG data, we aimed to enhance psychiatric disorder diagnosis, offering promising implications for medical advancements. We utilized a dataset of 945 individuals,... -
DR-GPT: A large language model for medical report analysis of diabetic retinopathy patients
Diabetic retinopathy (DR) is a sight-threatening condition caused by diabetes. Screening programmes for DR include eye examinations, where the patient's fundi are photographed, and the findings, including DR severity, are recorded in the medical report. However, statistical analyses based on DR severity require structured labels that calls for laborious manual annotation process if the report format is unstructured. In this work, we propose a large language model DR-GPT for classification of the... -
Computational imaging for rapid detection of grade-I cerebral small vessel disease (cSVD)
An early identification and subsequent management of cerebral small vessel disease (cSVD) grade 1 can delay progression into grades II and III. Machine learning algorithms have shown considerable promise in medical image interpretation automation. An experimental cross-sectional study aimed to develop an automated computer-aided diagnostic system based on AI (artificial intelligence) tools to detect grade 1-cSVD with improved accuracy. Patients with Fazekas grade 1 cSVD on Non-Contrast Magnetic... -
Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset
Cephalometric analysis is critically important and common procedure prior to orthodontic treatment and orthognathic surgery. Recently, deep learning approaches have been proposed for automatic 3D cephalometric analysis based on landmarking from CBCT scans. However, these approaches have relied on uniform datasets from a single center or imaging device but without considering patient ethnicity. In addition, previous works have considered a limited number of clinically relevant cephalometric... -
Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning
CONCLUSIONS: Our BDL-based analysis provides a first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation. -
ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review
Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and... -
Increased blood coagulation is associated with poor neurological outcome in aneurysmal subarachnoid hemorrhage
CONCLUSIONS: Patients were in a state of increased blood coagulation after aSAH, with those with unfavorable neurological outcome being more coagulable than those with favorable outcome. However, increased blood coagulation was not associated with DCI. -
Editorial: Supporting sustainable behavior change and empowerment in ubiquitous and learning health systems
No abstract -
Early detection of late-onset neonatal sepsis from noninvasive biosignals using deep learning: A multicenter prospective development and validation study
CONCLUSION: Raw biosignals from NICU monitors, in conjunction with PSD transformation, as input to the CNN, can provide state-of-the-art prediction performance for LOS without the need for artifact removal. To the knowledge of the authors, this is the first study to highlight the independent and additive predictive potential of electrocardiogram R-wave morphology and concurrent, sudden changes in the RESP waveform in predicting the onset of LOS using non-invasive biosignals. -
Cloud type classification using deep learning with cloud images
Clouds play a pivotal role in determining the weather, impacting the daily lives of everyone. The cloud type can offer insights into whether the weather will be sunny or rainy and even serve as a warning for severe and stormy conditions. Classified into ten distinct classes, clouds provide valuable information about both typical and exceptional weather patterns, whether they are short or long-term in nature. This study aims to anticipate cloud formations and classify them based on their shapes... -
Harnessing uncertainty in radiotherapy auto-segmentation quality assurance
No abstract -
Assessing the relevance of mental health factors in fibromyalgia severity: A data-driven case study using explainable AI
CONCLUSIONS: This study's findings, validated by clinicians, are potentially aligned with FM international guidelines that promote non-pharmacological interventions such as promoting mental well-being of FM patients. -
Outlier Analysis for Acute Blood Biomarkers of Moderate and Severe Traumatic Brain Injury
Blood biomarkers have been studied to improve the clinical assessment and prognostication of patients with moderate-severe traumatic brain injury (mo/sTBI). To assess their clinical usability, one needs to know of potential factors that might cause outlier values and affect clinical decision making. In a prospective study, we recruited patients with mo/sTBI (n = 85) and measured the blood levels of eight protein brain pathophysiology biomarkers, including glial fibrillary acidic protein (GFAP),... -
Late Blood Levels of Neurofilament Light Correlate With Outcome in Patients With Traumatic Brain Injury
Neurofilament light (NF-L) is an axonal protein that has shown promise as a traumatic brain injury (TBI) biomarker. Serum NF-L shows a rather slow rise after injury, peaking after 1-2 weeks, although some studies suggest that it may remain elevated for months after TBI. The aim of this study was to examine if plasma NF-L levels several months after the injury correlate with functional outcome in patients who have sustained TBIs of variable initial severity. In this prospective study of 178... -
International Multi-Specialty Expert Physician Preoperative Identification of Extranodal Extension n Oropharyngeal Cancer Patients using Computed Tomography: Prospective Blinded Human Inter-Observer Performance Evaluation
CONCLUSIONS AND RELEVANCE: Multiobserver testing shows physician discrimination of HPV+OPC pENE on pre-operative CT remains non-different than blind guessing, with high interrater variability and low diagnostic accuracy, regardless of clinician specialty. While minor differences in diagnostic performance among specialties are noted, they do not significantly affect the overall poor agreement and discrimination rates observed. The findings underscore the need for further research into automated...
2023
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Interactive AI annotation of medical images in a virtual reality environment
CONCLUSION: This study is the first to compare the quality of original and interactive AI annotation and to use radiologists' opinions as the measure. More research is needed for generalization. -
Harnessing uncertainty in radiotherapy auto-segmentation quality assurance
No abstract -
Enhancing Tissue Equivalence in <sup>7</sup>Li Heavy Ion Therapy with MC Algorithm Optimized Polymer-Based Bioinks
The unique physical properties of heavy ion beams, particularly their distinctive depth-dose distribution and sharp lateral dose reduction profiles, have led to their widespread adoption in tumor therapy worldwide. However, the physical properties of heavy ion beams must be investigated to deliver a sufficient dose to tumors without damaging organs at risk. These studies should be performed on phantoms made of biomaterials that closely mimic human tissue. Polymers can serve as soft tissue... -
Assessing the relevance of mental health factors in fibromyalgia severity: A data-driven case study using explainable AI
CONCLUSIONS: This study's findings, validated by clinicians, are potentially aligned with FM international guidelines that promote non-pharmacological interventions such as promoting mental well-being of FM patients. -
MC TRIM Algorithm in Mandibula Phantom in Helium Therapy
Helium ion beam therapy, one of the particle therapies developed and studied in the 1950s for cancer treatment, resulted in clinical trials starting at Lawrence Berkeley National Laboratory in 1975. While proton and carbon ion therapies have been implemented in research institutions and hospitals globally after the end of the trials, progress in comprehending the physical, biological, and clinical findings of helium ion beam therapy has been limited, particularly due to its limited... -
Outlier Analysis for Acute Blood Biomarkers of Moderate and Severe Traumatic Brain Injury
Blood biomarkers have been studied to improve the clinical assessment and prognostication of patients with moderate-severe traumatic brain injury (mo/sTBI). To assess their clinical usability, one needs to know of potential factors that might cause outlier values and affect clinical decision making. In a prospective study, we recruited patients with mo/sTBI (n = 85) and measured the blood levels of eight protein brain pathophysiology biomarkers, including glial fibrillary acidic protein (GFAP),... -
Late Blood Levels of Neurofilament Light Correlate With Outcome in Patients With Traumatic Brain Injury
Neurofilament light (NF-L) is an axonal protein that has shown promise as a traumatic brain injury (TBI) biomarker. Serum NF-L shows a rather slow rise after injury, peaking after 1-2 weeks, although some studies suggest that it may remain elevated for months after TBI. The aim of this study was to examine if plasma NF-L levels several months after the injury correlate with functional outcome in patients who have sustained TBIs of variable initial severity. In this prospective study of 178... -
Crohn's Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome
Human microbiota refers to the trillions of microorganisms that inhabit our bodies and have been discovered to have a substantial impact on human health and disease. By sampling the microbiota, it is possible to generate massive quantities of data for analysis using Machine Learning algorithms. In this study, we employed several modern Machine Learning techniques to predict Inflammatory Bowel Disease using raw sequence data. The dataset was obtained from NCBI preprocessed graph representations... -
Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset
Preoperative radiological identification of mandibular canals is essential for maxillofacial surgery. This study demonstrates the reproducibility of a deep learning system (DLS) by evaluating its localisation performance on 165 heterogeneous cone beam computed tomography (CBCT) scans from 72 patients in comparison to an experienced radiologist's annotations. We evaluated the performance of the DLS using the symmetric mean curve distance (SMCD), the average symmetric surface distance (ASSD), and... -
Improvement of a prediction model for heart failure survival through explainable artificial intelligence
Cardiovascular diseases and their associated disorder of heart failure (HF) are major causes of death globally, making it a priority for doctors to detect and predict their onset and medical consequences. Artificial Intelligence (AI) allows doctors to discover clinical indicators and enhance their diagnoses and treatments. Specifically, "eXplainable AI" (XAI) offers tools to improve the clinical prediction models that experience poor interpretability of their results. This work presents an... -
Plasma neurofilament light admission levels and development of axonal pathology in mild traumatic brain injury
CONCLUSION: In patients with mTBI, the significant correlation between NF-L levels at admission and diffusion tensor imaging (DTI) measurements of diffuse axonal injury (DAI) over more than 3 months suggests that the early levels of plasma NF-L may associate with the presence of DAI at a later phase of TBI. -
Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review
Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to... -
Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer
CONCLUSIONS AND RELEVANCE: This prognostic study's findings show external validation of a fully automated deep learning pipeline to accurately measure sarcopenia in HNSCC and an association with important disease outcomes. The pipeline could enable the integration of sarcopenia assessment into clinical decision making for individuals with HNSCC. -
Monte Carlo Simulation of TRIM Algorithm in Ceramic Biomaterial in Proton Therapy
Biomaterials play a crucial role in enhancing human health and quality of life. They are employed in applications such as tissue substitution, diagnostic tools, medical supplies, therapeutic treatments, regenerative medicine, and radiation dosimetric studies. However, their predisposition to proton therapy, which is a trending treatment in the world, has not been adequately studied. Ceramic biomaterials, known for their hardness and durability, offer versatile uses, especially in bone tissue... -
Associations Between Engagement with the BitHabit Digital Lifestyle Intervention and Changes in Type 2 Diabetes Risk Factors
Type 2 diabetes (T2D) can be prevented or delayed through a healthy lifestyle. Digital behavior change interventions (DBCIs) may offer cost-effective and scalable means to support lifestyle changes. This study investigated associations between user engagement with a habit-formation-based DBCI, the BitHabit app, and changes in T2D risk factors over 12 months in 963 participants at risk of T2D. User engagement was characterized by calculating use metrics from the BitHabit log data. User ratings... -
CYP3A4*22 may increase bleeding risk in ticagrelor users
No abstract -
Trajectories of interleukin 10 and heart fatty acid-binding protein levels in traumatic brain injury patients with or without extracranial injuries
CONCLUSION: Inflammatory reactions during the early days after a TBI do not significantly confound the use of IL-10 and H-FABP as TBI biomarkers. Extracranial injuries and cardiac sources may influence the levels of H-FABP in patients with moderate-to-severe TBI. -
Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer
Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard... -
Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline
CONCLUSION: We developed and externally validated a fully-automated platform that strongly correlates with imaging-assessed sarcopenia in patients with H&N cancer that correlates with survival and toxicity outcomes. This study constitutes a significant stride towards the integration of sarcopenia assessment into decision-making for individuals diagnosed with HNSCC. -
Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases
CONCLUSION: Defacing algorithms may have a significant impact on HNC OAR auto-segmentation model training and testing. This work highlights the need for further development of HNC-specific image anonymization methods. -
Application of simultaneous uncertainty quantification for image segmentation with probabilistic deep learning: Performance benchmarking of oropharyngeal cancer target delineation as a use-case
CONCLUSION: We found that many of the investigated methods provide overall similar but distinct utility in terms of predicting segmentation quality and referral performance. These findings are a critical first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation. -
Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records
With over 17 million annual deaths, cardiovascular diseases (CVDs) dominate the cause of death statistics. CVDs can deteriorate the quality of life drastically and even cause sudden death, all the while inducing massive healthcare costs. This work studied state-of-the-art deep learning techniques to predict increased risk of death in CVD patients, building on the electronic health records (EHR) of over 23,000 cardiac patients. Taking into account the usefulness of the prediction for chronic... -
A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models
Endoscopic procedures for diagnosing gastrointestinal tract findings depend on specialist experience and inter-observer variability. This variability can cause minor lesions to be missed and prevent early diagnosis. In this study, deep learning-based hybrid stacking ensemble modeling has been proposed for detecting and classifying gastrointestinal system findings, aiming at early diagnosis with high accuracy and sensitive measurements and saving workload to help the specialist and objectivity in... -
What Makes a Quality Health App-Developing a Global Research-Based Health App Quality Assessment Framework for CEN-ISO/TS 82304-2: Delphi Study
CONCLUSIONS: A useful and usable international standard for health app quality assessment was developed. Its quality, approval rate, and early use provide proof of its potential to become the trusted, commonly used global framework. The framework will help manufacturers enhance and efficiently demonstrate the quality of health apps, consumers, and health care professionals to make informed decisions on health apps. It will also help insurers to make reimbursement decisions on health apps.
2022
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Novel Sensors and Data-Driven Approach to Support Prevention, Diagnosis, and Treatment Planning of Cerebrovascular Accidents: Protocol for a Multimodal Data Collection Study With Novel Sensors
CONCLUSIONS: We successfully conducted a multicenter multimodal data collection study on healthy controls and patients with stroke and TIA. This protocol paper provides a comprehensive methodological description of the Stroke-data study, serving as a reference for previously published and future analyses based on the dataset. The protocol is unique in combining physiological sensor data, video recordings, clinical assessments, questionnaires, and health record data from patients with stroke and... -
Performance of the 12-lead ECG in predicting short- and long-term risk of sudden cardiac death
We evaluated the performance of 12-channel ECG in predicting sudden cardiac death across different time intervals using a retrospective data set of 17,625 high-risk cardiac patients who underwent coronary angiography (2007-2018) with follow-up data until 2022. Extreme gradient boosting using 12SL Marquette software-derived parameters from digital ECG recording was used to train and validate models using a random 80/20 split. Model performance was evaluated in both unbalanced and... -
Interactive 3D segmentation for primary gross tumor volume in oropharyngeal cancer
Radiotherapy is the main treatment modality of oropharyngeal cancer (OPC), in which an accurate segmentation of primary gross tumor volume (GTVt) is essential but also challenging due to significant interobserver variability and the time consumed in manual tumor delineation. For such a challenge an interactive deep learning (DL) based approach offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we investigate an... -
Cluster Analysis Reveals Subgroups with Different Risk Profiles and Sickness Absence Patterns in an Occupational Health Cohort
CONCLUSION: Unsupervised and supervised machine learning methods identified six clinically coherent employee clusters, providing information on typical combinations of characteristics and risk profiles of sickness absence. -
Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer
CONCLUSIONS AND RELEVANCE: This prognostic study's findings show external validation of a fully automated deep learning pipeline to accurately measure sarcopenia in HNSCC and an association with important disease outcomes. The pipeline could enable the integration of sarcopenia assessment into clinical decision making for individuals with HNSCC. -
Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients
This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the... -
Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study
Problems with fatigue and sleep are highly prevalent in patients with chronic diseases and often rated among the most disabling symptoms, impairing their activities of daily living and the health-related quality of life (HRQoL). Currently, they are evaluated primarily via Patient Reported Outcomes (PROs), which can suffer from recall biases and have limited sensitivity to temporal variations. Objective measurements from wearable sensors allow to reliably quantify disease state, changes in the... -
Comparison of deep learning segmentation and multigrader-annotated mandibular canals of multicenter CBCT scans
Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validation are scarce. To validate the mandibular canal localization accuracy of a deep learning system (DLS) we trained it with 982 CBCT scans and evaluated using 150 scans of five scanners from clinical workflow patients of European and Southeast Asian Institutes, annotated by four radiologists. The interobserver variability... -
Artificial Intelligence for Radiation Oncology Applications Using Public Datasets
Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation oncology. However, large curated datasets - often involving imaging data and corresponding annotations - are required to develop radiation oncology AI models. Importantly, the recent establishment of Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management have enabled an increasing number of radiation oncology related datasets to be disseminated through data... -
Deep learning auto-segmentation of cervical skeletal muscle for sarcopenia analysis in patients with head and neck cancer
CONCLUSION: We developed a high-performance, multi-stage, fully-automated approach to segment cervical vertebra SM. Our study is an essential step towards fully-automated sarcopenia-related decision-making in patients with HNC. -
Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer
The accurate determination of sarcopenia is critical for disease management in patients with head and neck cancer (HNC). Quantitative determination of sarcopenia is currently dependent on manually-generated segmentations of skeletal muscle derived from computed tomography (CT) cross-sectional imaging. This has prompted the increasing utilization of machine learning models for automated sarcopenia determination. However, extant datasets currently do not provide the necessary manually-generated... -
Evaluation of Machine Learning Algorithms and Explainability Techniques to Detect Hearing Loss From a Speech-in-Noise Screening Test
CONCLUSIONS: This study demonstrates that a multivariate approach can help detect hearing loss with satisfactory performance. Further research on a bigger sample and using more complex ML algorithms and explainability techniques is needed to fully investigate the role of input features (including additional features such as risk factors and individual responses to low-/high-frequency stimuli) in predicting hearing loss. -
Auto-detection and segmentation of involved lymph nodes in HPV-associated oropharyngeal cancer using a convolutional deep learning neural network
CONCLUSION: The results from this single-institution study demonstrate the successful automation of lymph node segmentation for patients with HPV-OPC using a DL-CNN. Future studies, including validation with an external dataset, are necessary to clarify its role in the larger radiation oncology treatment planning workflow. -
Mobilemicroservices Architecture for Remote Monitoring of Patients: A Feasibility Study
Recent developments in smart mobile devices (SMDs), wearable sensors, the Internet, mobile networks, and computing power provide new healthcare opportunities that are not restricted geographically. This paper aims to introduce Mobilemicroservices Architecture (MMA) based on a study on architectures. In MMA, an HTTP-based Mobilemicroservivce (MM) is allocated to each SMD's sensor. The key benefits are extendibility, scalability, ease of use for the patient, security, and the possibility to... -
Trends in the national early warning score are associated with subsequent mortality - A prospective three-centre observational study with 11,331 general ward patients
CONCLUSION: The NEWS score trajectory during the first three days of general ward admission is associated with patient outcome. Further studies are warranted to determine specific thresholds for clinically relevant changes in the NEWS trajectories. -
Development Towards Patient-Centered eHealth Services in Finland
The paper analyses the development of public eHealth services from 2014 to 2021 from the patients' point of view. The merits and missing features of the eHealth services were identified with patient interviews in 2014-2015. The list of missing features was again checked against the eHealth services in 2021. The main finding was that all the features wanted by the patients had still not been implemented. The finding of this paper suggests that current Finnish public eHealth services are... -
A smart hospital-driven approach to precision pharmacovigilance
Researchers, regulatory agencies, and the pharmaceutical industry are moving towards precision pharmacovigilance as a comprehensive framework for drug safety assessment, at the service of the individual patient, by clustering specific risk groups in different databases. This article explores its implementation by focusing on: (i) designing a new data collection infrastructure, (ii) exploring new computational methods suitable for drug safety data, and (iii) providing a computer-aided framework... -
Ability of the National Early Warning Score and its respiratory and haemodynamic subcomponents to predict short-term mortality on general wards: a prospective three-centre observational study in Finland
CONCLUSIONS: NEWS discriminated general ward patients at risk for acute death with excellent statistical accuracy. The respiratory rate component is especially strongly associated with short-term mortality. -
Digitally Supported Lifestyle Intervention to Prevent Type 2 Diabetes Through Healthy Habits: Secondary Analysis of Long-Term User Engagement Trajectories in a Randomized Controlled Trial
CONCLUSIONS: Distinct long-term usage trajectories of the BitHabit app were identified, and individual predictors for belonging to different trajectory groups were found. These findings highlight the need for being able to identify individuals likely to disengage from interventions early on, and could be used to inform the development of future adaptive interventions. -
Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health
We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032... -
Integrating data from multiple Finnish biobanks and national health-care registers for retrospective studies: Practical experiences
Aim: This case study aimed to investigate the process of integrating resources of multiple biobanks and health-care registers, especially addressing data permit application, time schedules, co-operation of stakeholders, data exchange and data quality. Methods: We investigated the process in the context of a retrospective study: Pharmacogenomics of antithrombotic drugs (PreMed study). The study involved linking the genotype data of three Finnish biobanks (Auria Biobank, Helsinki Biobank and THL...
2021
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Novel Sensors and Data-Driven Approach to Support Prevention, Diagnosis, and Treatment Planning of Cerebrovascular Accidents: Protocol for a Multimodal Data Collection Study With Novel Sensors
CONCLUSIONS: We successfully conducted a multicenter multimodal data collection study on healthy controls and patients with stroke and TIA. This protocol paper provides a comprehensive methodological description of the Stroke-data study, serving as a reference for previously published and future analyses based on the dataset. The protocol is unique in combining physiological sensor data, video recordings, clinical assessments, questionnaires, and health record data from patients with stroke and... -
Interactive 3D segmentation for primary gross tumor volume in oropharyngeal cancer
Radiotherapy is the main treatment modality of oropharyngeal cancer (OPC), in which an accurate segmentation of primary gross tumor volume (GTVt) is essential but also challenging due to significant interobserver variability and the time consumed in manual tumor delineation. For such a challenge an interactive deep learning (DL) based approach offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we investigate an... -
Application of simultaneous uncertainty quantification for image segmentation with probabilistic deep learning: Performance benchmarking of oropharyngeal cancer target delineation as a use-case
CONCLUSION: We found that many of the investigated methods provide overall similar but distinct utility in terms of predicting segmentation quality and referral performance. These findings are a critical first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation. -
What Makes a Quality Health App-Developing a Global Research-Based Health App Quality Assessment Framework for CEN-ISO/TS 82304-2: Delphi Study
CONCLUSIONS: A useful and usable international standard for health app quality assessment was developed. Its quality, approval rate, and early use provide proof of its potential to become the trusted, commonly used global framework. The framework will help manufacturers enhance and efficiently demonstrate the quality of health apps, consumers, and health care professionals to make informed decisions on health apps. It will also help insurers to make reimbursement decisions on health apps. -
Development Towards Patient-Centered eHealth Services in Finland
The paper analyses the development of public eHealth services from 2014 to 2021 from the patients' point of view. The merits and missing features of the eHealth services were identified with patient interviews in 2014-2015. The list of missing features was again checked against the eHealth services in 2021. The main finding was that all the features wanted by the patients had still not been implemented. The finding of this paper suggests that current Finnish public eHealth services are... -
SPECT Image Features for Early Detection of Parkinson's Disease using Machine Learning Methods
Millions of people around the world suffer from Parkinson's disease, a neurodegenerative disorder with no remedy. Currently, the best response to interventions is achieved when the disease is diagnosed at an early stage. Supervised machine learning models are a common approach to assist early diagnosis from clinical data, but their performance is highly dependent on available example data and selected input features. In this study, we explore 23 single photon emission computed tomography (SPECT)... -
Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health
We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032... -
Machine learning model predicts short-term mortality among prehospital patients: A prospective development study from Finland
CONCLUSIONS: Among unselected prehospital patients, a machine learning model including blood glucose and NEWS parameters had a fair performance in predicting 30-day mortality. -
Pharmacogenetics of Bleeding and Thromboembolic Events in Direct Oral Anticoagulant Users
This study aimed to analyze associations between genetic variants and the occurrence of clinical outcomes in dabigatran, apixaban, and rivaroxaban users. This was a retrospective real-world study linking genotype data of three Finnish biobanks with national register data on drug dispensations and healthcare encounters. We investigated several single-nucleotide variants (SNVs) in the ABCG2, ABCB1, CES1, and CYP3A5 genes potentially associated with bleeding or thromboembolic events in direct oral... -
Integrating data from multiple Finnish biobanks and national health-care registers for retrospective studies: Practical experiences
Aim: This case study aimed to investigate the process of integrating resources of multiple biobanks and health-care registers, especially addressing data permit application, time schedules, co-operation of stakeholders, data exchange and data quality. Methods: We investigated the process in the context of a retrospective study: Pharmacogenomics of antithrombotic drugs (PreMed study). The study involved linking the genotype data of three Finnish biobanks (Auria Biobank, Helsinki Biobank and THL... -
Evaluation of machine learning algorithms for health and wellness applications: A tutorial
Research on decision support applications in healthcare, such as those related to diagnosis, prediction, treatment planning, etc., has seen strongly growing interest in recent years. This development is thanks to the increase in data availability as well as to advances in artificial intelligence and machine learning research and access to computational resources. Highly promising research examples are published daily. However, at the same time, there are some unrealistic, often overly... -
Pharmacogenetics of Anticoagulation and Clinical Events in Warfarin-Treated Patients: A Register-Based Cohort Study with Biobank Data and National Health Registries in Finland
CONCLUSION: In a real-world setting, genetically sensitive and highly sensitive responders to warfarin had more high INR tests and required a lower daily dose of warfarin than normal responders. However, the risk for bleeding events was not increased in sensitive and highly sensitive responders. Interestingly, the risk of thromboembolic outcomes was lower in normal responders compared to the sensitive and highly sensitive responders. -
A deep learning approach for sepsis monitoring via severity score estimation
CONCLUSIONS: By utilizing SOFA scores, our framework facilitates the prognose of sepsis and infected organ systems state. While previous studies focused only on predicting presence of sepsis, our model aims at providing a prognosis solution for sepsis. SOFA score estimation process in ICU depends on laboratory environment. This dependence causes delays in treating patients, which in turn may increase the risk of complications. By using easily accessible non-invasive vital signs that are...
2020
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Blood biomarker outliers and clinical events during intensive care in patients with traumatic brain injury
INTRODUCTION: Monitoring and timely clinical assessment are fundamental to treating patients with traumatic brain injury (TBI) in the intensive care unit (ICU). Proactive, individualised treatment is unavailable with the current methods. -
Blood biomarkers to identify patients with different intracranial lesion combinations after traumatic brain injury
INTRODUCTION: There is a lack of studies examining the most promising blood biomarkers for traumatic brain injury (TBI) in relation to gross pathology types. -
Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients
This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the... -
Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study
CONCLUSION: A random forest algorithm trained with NEWS variables was superior to traditional NEWS for predicting one-day mortality in adult prehospital patients, although the risk of selection bias must be acknowledged. The inclusion of blood glucose in the model further improved its predictive performance. -
Admission Levels of Interleukin 10 and Amyloid β 1-40 Improve the Outcome Prediction Performance of the Helsinki Computed Tomography Score in Traumatic Brain Injury
Background: Blood biomarkers may enhance outcome prediction performance of head computed tomography scores in traumatic brain injury (TBI). Objective: To investigate whether admission levels of eight different protein biomarkers can improve the outcome prediction performance of the Helsinki computed tomography score (HCTS) without clinical covariates in TBI. Materials and methods: Eighty-two patients with computed tomography positive TBIs were included in this study. Plasma levels of β-amyloid... -
A Social Network Analysis of Tweets Related to Masks during the COVID-19 Pandemic
CONCLUSION: Towards the end of June 2020, Twitter was utilized by the public to encourage others to wear masks and discussions around masks included a wide range of users. -
A deep learning approach for sepsis monitoring via severity score estimation
CONCLUSIONS: By utilizing SOFA scores, our framework facilitates the prognose of sepsis and infected organ systems state. While previous studies focused only on predicting presence of sepsis, our model aims at providing a prognosis solution for sepsis. SOFA score estimation process in ICU depends on laboratory environment. This dependence causes delays in treating patients, which in turn may increase the risk of complications. By using easily accessible non-invasive vital signs that are... -
cCOG: A web-based cognitive test tool for detecting neurodegenerative disorders
INTRODUCTION: Web-based cognitive tests have potential for standardized screening in neurodegenerative disorders. We examined accuracy and consistency of cCOG, a computerized cognitive tool, in detecting mild cognitive impairment (MCI) and dementia. -
Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline
The importance of early interventions in Alzheimer's disease (AD) emphasizes the need to accurately and efficiently identify at-risk individuals. Although many dementia prediction models have been developed, there are fewer studies focusing on detection of brain pathology. We developed a model for identification of amyloid-PET positivity using data on demographics, vascular factors, cognition, APOE genotype, and structural MRI, including regional brain volumes, cortical thickness and a visual... -
Parkinson's disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study
Parkinson's disease (PD) is a neurodegenerative disease inducing dystrophy of the motor system. Automatic movement analysis systems have potential in improving patient care by enabling personalized and more accurate adjust of treatment. These systems utilize machine learning to classify the movement properties based on the features derived from the signals. Smartphones can provide an inexpensive measurement platform with their built-in sensors for movement assessment. This study compared three... -
Gait Disturbances are Associated with Increased Cognitive Impairment and Cerebrospinal Fluid Tau Levels in a Memory Clinic Cohort
CONCLUSION: These findings suggest that gait - particularly measures related to pace and rhythm - are altered in dementia and have a direct link with measures of neurodegeneration. -
Interleukin 10 and Heart Fatty Acid-Binding Protein as Early Outcome Predictors in Patients With Traumatic Brain Injury
Background: Patients with traumatic brain injury (TBI) exhibit a variable and unpredictable outcome. The proteins interleukin 10 (IL-10) and heart fatty acid-binding protein (H-FABP) have shown predictive values for the presence of intracranial lesions. Aim: To evaluate the individual and combined outcome prediction ability of IL-10 and H-FABP, and to compare them to the more studied proteins S100β, glial fibrillary acidic protein (GFAP), and neurofilament light (NF-L), both with and without... -
The Definition of Informatics Competencies in Finnish Healthcare and Social Welfare Education
Finland is a world leader in the use of public electronic services. Continuous improvement to competencies is a prerequisite for the success of digitalisation in the service development sector. The increasing use of information technology in health and social care needs to be taken into account in the education of the health and social care sector work force. The mandate of the national SotePeda 24/7 project is to identify and define the informatics competencies required for multidisciplinary... -
Monitoring and Intervention Technologies to Manage Diabetic Older Persons: The CAPACITY Case-A Pilot Study
Diabetes Mellitus is a chronic disease with a high prevalence among older people, and it is related to an increased risk of functional and cognitive decline, in addition to classic micro and macrovascular disease and a moderate increase in the risk of death. Technology aimed to improve elder care and quality of life needs to focus in the early detection of decline, monitoring the functional evolution of the individuals and providing ways to foster physical activity, to recommend adequate... -
Admission Levels of Total Tau and β-Amyloid Isoforms 1-40 and 1-42 in Predicting the Outcome of Mild Traumatic Brain Injury
Background: The purpose of this study was to investigate if admission levels of total tau (T-tau) and β-amyloid isoforms 1-40 (Aβ40) and 1-42 (Aβ42) could predict clinical outcome in patients with mild traumatic brain injury (mTBI). Methods: A total of 105 patients with mTBI [Glasgow Coma Scale (GCS) ≥ 13] recruited in Turku University Hospital, Turku, Finland were included in this study. Blood samples were drawn within 24 h of admission for analysis of plasma T-tau, Aβ40, and Aβ42. Patients... -
Deep Learning Method for Mandibular Canal Segmentation in Dental Cone Beam Computed Tomography Volumes
Accurate localisation of mandibular canals in lower jaws is important in dental implantology, in which the implant position and dimensions are currently determined manually from 3D CT images by medical experts to avoid damaging the mandibular nerve inside the canal. Here we present a deep learning system for automatic localisation of the mandibular canals by applying a fully convolutional neural network segmentation on clinically diverse dataset of 637 cone beam CT volumes, with mandibular... -
Metabolic Profiles Help Discriminate Mild Cognitive Impairment from Dementia Stage in Alzheimer's Disease
Accurate differentiation between neurodegenerative diseases is developing quickly and has reached an effective level in disease recognition. However, there has been less focus on effectively distinguishing the prodromal state from later dementia stages due to a lack of suitable biomarkers. We utilized the Disease State Index (DSI) machine learning classifier to see how well quantified metabolomics data compares to clinically used cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD).... -
FriWalk robotic walker: usability, acceptance and UX evaluation after a pilot study in a real environment
Background: Scientific evidence supports that prevention strategies like multicomponent physical exercise help avoiding functional decline, falls and frailty. The robotic walker FriWalk, developed within the ACANTO project, supports the execution of controlled physical activities during hospital admission to prevent functional deterioration associated to prolonged bedrest. FriWalk shows in a clinical validation study a positive relationship with improvement in physical performance, basic...