Exercise & Fitness

A Deep Learning–Based Automated Detection of Mucus Plugs in Chest CT

This study presents a novel two stage deep learning algorithm for automated detection of mucus plugs in CT scans of patients with respiratory diseases. Despite the clinical significance of mucus plugs in COPD and asthma where they indicate hypoxemia, reduced exercise tolerance, and poorer outcomes, they remain under evaluated in clinical practice due to labor intensive manual annotation. The devel...

Paving the way for precision treatment of psychiatric symptoms with functional connectivity neurofeedback

Major depressive disorder (MDD) remains challenging to treat, with many patients failing to respond adequately to existing therapies. Patients with MDD have heterogeneous subsets of symptoms with differing underlying neural aberrations. Treatment response may improve if treatments become more individualised. We recently showed preliminary evidence that normalisation of a neural network and a corre...

Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals

Schizophrenia and bipolar disorder are severe mental illnesses that significantly impact quality of life. These disorders are associated with autonomi...

The gSOS Polygenic Score is Associated with Bone Density and Fracture Risk in Childhood

The polygenic risk score genetic quantitative ultrasound speed of sound (gSOS) was developed using machine learning algorithms in adults of European a...

GlucoseGo: A Simple, User-Friendly, Machine Learning-Derived Tool for Predicting Exercise-Related Hypoglycaemia Risk in Type 1 Diabetes

This study aims to develop an accessible, machine learning-derived tool for people with type 1 diabetes that predicts hypoglycaemia risk at the start ...

Development and accuracy of a novel machine learning model to detect toddlers’ physical activity and sedentary time using accelerometers: Little Movers Activity Analysis

Objective: (1) develop and test a novel, open-source, supervised machine learning model to detect toddlers’ physical activity (PA) and sedentary time ...

Medication information extraction using local large language models

Medication information is crucial for clinical routine and research. However, a vast amount is stored in unstructured text, such as doctoral letters, ...

Reliable Radiologic Skeletal Muscle Area Assessment – A Biomarker for Cancer Cachexia Diagnosis

Cancer cachexia is a common metabolic disorder characterized by severe muscle atrophy which is associated with poor prognosis and quality of life. Mon...

Extracting Carotid Stenosis Severity from Clinical Notes Using Natural Language Processing: Development, Validation, and Application in a Nationwide Veteran Cohort

Carotid stenosis, which is atherosclerotic narrowing of the extracranial carotid arteries, is an important risk factor for ischemic stroke. The preval...

Key predictors of maternal mild depression and anxiety in low resource settings: A machine learning approach

Maternal mental health (MMH) disorders, particularly depression and anxiety, are major public health concerns in low- and middle-income countries (LMI...

Dense sampling of choices links high learning rates to obesity and low reward sensitivity to binge eating

Mounting evidence shows that obesity is associated with alterations in dopamine transmission. However, in humans, corresponding changes in dopamine-de...

Speaking the Language of Inclusion: Examining English Languages Requirements in Cardiovascular Digital Health Trials

Cardiovascular medicine is rapidly evolving, as it integrates digital technologies intended to decentralize care from the clinic and/or hospital setti...

Machine Learning Identifies Microbiome and Clinical Predictors of Sustained Weight Loss Following Prolonged Fasting

Prolonged fasting may benefit metabolic health, but data in healthy individuals remain limited. We performed a randomized, waitlist-controlled study (...

Enhancing Fairness in Diabetes Prediction Systems through Smart User Interface Design

Artificial intelligence (AI) in chronic disease prediction often exhibits algorithmic biases, hindering equitable healthcare delivery. This study aims...

Deep learning-enabled MRI phenotyping uncovers regional body composition heterogeneity and disease associations in two European population cohorts

Body mass index (BMI) does not account for substantial inter-individual differences in regional fat and muscle compartments, which are relevant for th...

Bridging the Heterogeneity of Myasthenia Gravis Severity Scores for Digital Twin Development

Myasthenia gravis (MG) is a rare autoimmune neuromuscular disease. Clinical trials with rigorously collected data, especially for rare diseases, provi...

Artificial Intelligence Enabled Phenogrouping of Heart Failure with Preserved Ejection Fraction Depicts Early and End-Stage Trajectories

Heart failure with preserved ejection fraction is challenging to diagnose, precluding the initiation of prognostic medications. A deeper understanding...

Hybrid Fuzzy Logic and Logistic Regression Model with Recursive Feature Elimination for Enhanced Prediction and Clinical Decision Support in Type 2 Diabetes Mellitus Among Adults Aged 35 to 45

This paper presents a hybrid model combining fuzzy logic, recursive feature elimination (RFE), and logistic regression to predict type 2 diabetes mell...

Development of a pilot machine learning model to predict successful cure in critically ill patients with community-acquired pneumonia

Severe community-acquired pneumonia (CAP) remains a major cause of critical illness, yet there are no validated early clinical criteria to predict sho...

Machine Learning Fairness in Predicting Underweight, Overweight and Adiposity Across Socioeconomic and Caste Group in India: Evidence from the Longitudinal Ageing Study in India

Machine learning (ML) models are widely used to predict body mass index (BMI), yet their fairness across socioeconomic and caste groups remains uncert...