Primary Care

Estimation of cycloplegic spherical refraction from non-cycloplegic clinical and biometric data in children using machine learning: a retrospective pilot study for screening triage.

OBJECTIVE: To develop and evaluate non-cycloplegic clinical and biometric models for estimating cycloplegic spherical power (DS), spherical equivalent (SE), and astigmatic power-vector components (J0 and J45) in children, and to assess their utility as a preliminary screening-triage aid rather than a substitute for cycloplegic refraction. METHODS: This retrospective pilot study included 590 childr...

A Robust Voice-Based Parkinson's Disease Diagnosis Model via Semantic RGB Feature Transformation and Hybrid Deep Learning.

Reliable diagnosis of Parkinson's disease (PD) in its early stages is of vital importance for supporting timely clinical decision-making and improving patient management. In this study, we propose a non-invasive, AI-supported hybrid diagnostic model for PD based on acoustic features extracted from voice recordings. Using a dataset created from voice recordings of 188 Parkinson's patients and 64 he...

Feasibility of image-based 3D avatar anthropometry assessment of pediatric obesity: a pilot study.

UNLABELLED: The purpose of this study is to evaluate the feasibility and validity of 3D avatar-based anthropometry for assessing anthropometric indica...

Prediction of hypertension and restenosis under guideline-directed management in aortic coarctation: development and validation of machine-learning models.

BACKGROUND: Aortic coarctation (CoA) is a major cause of arterial hypertension in young individuals, with recurrence occurring in up to one-third of p...

An interpretable predictive model for depression risk in diabetic patients: A web-based application using NHANES data.

Depression is a common comorbidity in individuals with diabetes and is associated with adverse clinical outcomes. Early identification of high-risk in...

Machine learning-based early kidney stone risk classification in diabetic-hypertensive patients using obesity composite indices.

Kidney stone disease (KSD) is increasingly prevalent among patients with diabetes mellitus and hypertension. Obesity-related metabolic abnormalities m...

Integrative analysis of machine learning and single-cell sequencing identifies IGHG1 as a therapeutic target for epimedium in diabetes-gastric cancer comorbidity.

BACKGROUND: The clinical comorbidity of diabetes mellitus (DM) and gastric cancer (GC) presents a significant healthcare challenge, as these two condi...

Consistent analgesic effect of intravenous dexamethasone on rebound pain after brachial plexus block: a causal machine learning approach.

BACKGROUND: Rebound pain, a sudden increase in pain intensity after resolution of regional anesthesia, remains a major challenge in postoperative pati...

Augmenting Pediatric Care With Artificial Intelligence: Opportunities and Responsibilities for Nurse Practitioners.

BACKGROUND: Artificial intelligence (AI) is emerging as a critical tool for nurse practitioners delivering pediatric and adolescent and young adult (A...

Calibration of Self-Reported Confidence and Accuracy of Large Language Models in Medical Question Answering.

OBJECTIVE: To evaluate the alignment between self-reported confidence of large language models (LLM) and their accuracy in answering medical multiple-...

Machine learning-driven prioritization and experimental validation of traditional Chinese medicine-derived STING-inhibitory candidates.

The stimulator of interferon genes (STING) is a key signalling adaptor in the cGAS-STING pathway of the innate immune system and plays a significant r...

Remote Assessment of Parkinson Disease Using Deep Learning on Structured Mouse-Trace Data From Suspected Cases: Machine-Learning Pilot Feasibility Study.

BACKGROUND: Parkinson disease (PD) is a pervasive neurodegenerative disorder globally, largely characterized by motor symptoms. Most existing artifici...

First-Pass Reperfusion After Endovascular Thrombectomy: A Real-World Analysis with Explainable Machine Learning for Intra-Procedural Decision Support.

OBJECTIVE: To evaluate determinants of first-pass reperfusion and to develop an explainable machine learning framework for intra-procedural decision s...

Using machine learning to identify individuals at elevated risk of diabetes from low-cost complete blood count data for opportunistic screening.

Type 2 diabetes and prediabetes remain substantially underrecognized, highlighting the need for practical screening approaches based on routinely avai...

Interpretable machine learning reveals the synergistic impact of essential hypertension on early recurrence in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) patients often ential hypertension, yet how systemic vascular stress synergizes with tumor aggressiveness to driv...

Implementation of artificial intelligence in healthcare in Aotearoa New Zealand: learnings from the diabetic retinal screening use case.

Artificial intelligence (AI) tools in diabetic retinal screening (DRS) are currently in use overseas within public health systems, with growing eviden...

A single-camera video-based assessment of locomotive syndrome using pose-silhouette fusion model.

Locomotive syndrome (LS), characterized by declining mobility due to musculoskeletal disorders, significantly affects older adults and is highly preva...

Artificial intelligence-based prediction of diseases among homeless populations in Bogotá: Implications for targeted interventions.

BACKGROUND: The homeless population in Bogotá exhibits complex social and health vulnerabilities, with a high prevalence of chronic and communicable d...

Immunosuppressive regimens and long-term kidney transplant outcomes: A dual survival modeling framework.

Optimizing immunosuppressive therapy remains central to improving long-term outcomes after kidney transplantation. Both induction and maintenance ther...

Development and validation of an explainable machine learning model for predicting acute kidney injury in critically ill patients with primary peritonitis: a multicenter cohort study.

Primary peritonitis-associated acute kidney injury (PPA-AKI) represents a complex complication that substantially elevates mortality risk. Currently, ...