Primary Care

An interpretable machine learning model for diabetic foot risk classification in patients with diabetes.

Diabetic foot is a severe chronic complication of diabetes, mainly resulting from peripheral neuropathy and vasculopathy, and may progress to ulcers, infections, and amputation if not treated in a timely manner. Early identification of patients who already present a high-risk diabetic foot profile at clinical evaluation is therefore critical for guiding preventive interventions. This study aimed t...

Innovative screening for OSAS in patients with head and neck cancer using the apneal application: Protocol for a prospective single-centre study.

INTRODUCTION: OSAS is a common yet underdiagnosed condition, particularly among patients with head and neck cancers (HNC). Anatomical changes caused by the tumor or its treatments-surgery, radiotherapy, or chemotherapy-can promote the onset of OSAS. However, fatigue in these patients is often attributed solely to the cancer treatment itself, which may delay appropriate diagnosis and management. ME...

Artificial intelligence in cervical cancer screening and triage: a role-stratified systematic review and bivariate meta-analysis.

PURPOSE OF REVIEW: Artificial intelligence (AI) tools for cervical cancer screening have proliferated, but modality-pooled accuracy estimates conflate...

Transforming Systematic Reviews: Evaluating a Fine-Tuned Large Language Model for Abstract Screening in Uveitis and Retinal Vasculitis: Fine-Tuned LLM for Review Screening.

PURPOSE: To evaluate the classification performance of UveAItis, a domain-specific large language model (LLM) fine-tuned for automated title and abstr...

Transformer-accelerated discovery of inhibitors targeting the RpsAΔ438 deletion in PZA-resistant tuberculosis.

Tuberculosis is a major global public health problem caused by Mycobacterium tuberculosis. Pyrazinamide (PZA), a key first-line drug, is activated to ...

Wearable devices for atrial fibrillation: diagnostic and screening roles of ECG and PPG-A systematic review.

OBJECTIVE: Atrial fibrillation (AF) is a major predictor of heart failure, stroke, and mortality. Traditional Holter monitors and event recorders are ...

Development and Validation of Machine-Learning-Based Prediction Models for Thyroid Diseases During Pregnancy.

BACKGROUND International guidelines recommend early screening based on targeted risk factors to identify thyroid disease during pregnancy. The complex...

A deep learning framework for quantification of hepatic steatosis in whole-slide images by tile-level scoring and screening.

Objective.To develop a deep learning framework for the efficient and accurate quantification of hepatic steatosis in whole-slide images (WSIs).Approac...

Super learner model for predicting carotid plaque regression in type 2 diabetes on PCSK9 inhibitors.

OBJECTIVES: Evidence regarding the effects of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors on carotid plaque regression in patient...

Development and validation of a multimodal machine learning prediction model for heart failure after acute myocardial infarction.

Heart failure after acute myocardial infarction (post-MI HF) has become a major global health problem. Accurate risk prediction is essential for optim...

[More than just technology: autonomous AI in ophthalmological care].

Autonomous artificial intelligence (AI) systems are finding their way into ophthalmological care, particularly in imaging diagnostics (fundus photogra...

EMBC Special Issue: Non-invasive BP Estimation via Carotid-Brachial-Radial Coupling: A Physics-Informed Asymmetric Cycle Network Approach.

Photoplethysmogram (PPG) based cuffless blood pressure (BP) monitoring holds significant potential to revolutionize hypertension management. However, ...

Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.

BACKGROUND: Functional impairments associated with mental health conditions are on the rise. Predicting functional outcomes may improve the targeting ...

AAPM Consensus Guidelines on Neuromodulation Technologies and Neurocomputer Interfaces for Pain Management and Functional Recovery.

OBJECTIVE: To provide an evidence-based framework for healthcare professionals to use neuromodulation technologies to restore neuromuscular function a...

Machine Learning Prediction of Heart Failure Readmissions: Insights From a Multicenter Emergency Department Trial.

BACKGROUND: Heart failure (HF) readmissions remain common and costly, yet existing prediction models show limited clinical utility, particularly at th...

Integrating artificial intelligence and machine learning into risk prediction for type 2 diabetes: A model for early identification of high-risk populations.

Family history, body mass index (BMI), and ethnicity are three key, well-established determinants of susceptibility to type 2 diabetes mellitus (T2DM)...

Trends in the use of adult-specific preference-weighted health-related quality of life instruments in clinical trials over the past 50 years: a protocol for a meta-research study using deep learning-based natural language processing and large language models.

BACKGROUND: Health technology assessment bodies increasingly emphasise the importance of preference-weighted health-related quality of life (HRQoL) ev...

A scoping review of the use of artificial intelligence as a psychological assessment tool.

Mental health disorders are highly prevalent worldwide, yet access to timely and effective mental health assessment (and care) remains limited. Artifi...

Deep learning-enabled single-cell morpholomic atlas of nasal swabs distinguishes chronic inflammation from sinonasal malignancy.

Sinonasal malignancies frequently present with symptoms overlapping chronic inflammatory conditions, complicating early detection and delaying treatme...

An interpretable combinatorial data-mining framework for predicting new-onset hypertension in the general population.

We previously established an interpretable combinatorial data-mining framework to identify combinations of clinical factors predictive of heart failur...