Refractive Surgery

Automated Prediction of Glasgow Coma Scale Scores From Unstructured Electronic Health Records Using Natural Language Processing: Development and Validation Study.

BACKGROUND: Multicenter electronic health records (EHRs) can support quality improvement and comparative effectiveness research in critical care. However, limitations of EHR-based research include challenges in abstracting key clinical variables, including a patient's level of consciousness. OBJECTIVE: This study aimed to develop a natural language processing model to predict Glasgow Coma Scale (G...

Somatic variant-calling beyond cancer: Repurposing algorithms to map low‑allele‑fraction variants across genomics.

BACKGROUND: Somatic variant callers were originally developed to identify tumour-specific mutations in mixed tumour-normal samples. Increasingly, disciplines such as developmental biology, reproductive medicine, virology and mitochondrial genetics require detection of low-frequency variants from high-depth sequencing. Many laboratories therefore reuse cancer callers without clear guidance on their...

Development of a deep learning model to predict cycloplegic myopia and pre- myopia: based on a large-scale screening of school-aged children.

PURPOSE: This study aims to develop and validate practical deep learning (DL) and machine learning (ML) models to predict cycloplegic myopia, pre-myop...

Identifying subgroups of ICU patients with high mortality rates using machine learning: A nationwide, population-based study.

PURPOSE: Identifying subgroups of intensive care unit (ICU) patients with high mortality rates can provide directions in policy making about appropria...

TACE-induced liver injury in hepatocellular carcinoma: mechanisms, prediction, and prevention in the era of gut microbiota and inflammasome research.

Transarterial chemoembolization (TACE) is a cornerstone locoregional therapy for hepatocellular carcinoma (HCC), yet most candidates also have cirrhos...

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...

Artificial intelligence-based quantitative computed tomography assessment of shoulder muscle and its association with outcomes after reverse total shoulder arthroplasty.

BACKGROUND: Muscle atrophy and fatty infiltration of the shoulder muscles are known to influence clinical outcomes after reverse total shoulder arthro...

Molecular, cellular and network mapping of brain structural deviations in patients with Post-COVID19 syndrome.

Post-COVID-19 syndrome encompasses persistent cognitive, neurological, and psychiatric symptoms following SARS-CoV-2 infection, profoundly affecting g...

AI-assisted digital breast tomosynthesis as a decision-support tool for post-neoadjuvant surgical planning in breast cancer.

PURPOSE: Accurate assessment of residual disease after neoadjuvant chemotherapy (NAC) is essential for surgical planning and prevention of incomplete ...

Combining structural and microvascular parameters via machine learning for enhanced diagnosis of normal-tension glaucoma among highly myopic eyes: a prospective cross-sectional diagnostic accuracy study.

PURPOSE: This study aimed to evaluate whether a combination of optical coherence tomography (OCT) and OCT angiography (OCTA) parameters could improve ...

Development and validation of a multimodal artificial intelligence-based model for predicting post-prostatectomy treatment outcomes from baseline biparametric prostate magnetic resonance imaging.

PURPOSE: Prostate cancer (PCa) is the second most common cancer and cause of cancer deaths among American men. Existing risk prediction methods have l...

Is cellular senescence a biological feature of Long COVID? A transcriptomic analysis across comparative post-acute sequelae phenotypes.

INTRODUCTION: Cellular senescence, involving cell-cycle arrest and inflammatory factor release, may play a role in Long COVID development. We investig...

Artificial Intelligence and Algorithmic Bias in Burn Care: A Literature Review of Ethical Challenges.

Artificial intelligence (AI) is increasingly integrated into burn care for triage, burn-depth assessment, prognostic scoring, pain management, and tel...

Quantitative Impact of T1 Subtraction Maps on Enhancing Component Delineation and Measured Volumes in Minimally Enhancing Pediatric Brain Tumors.

BACKGROUND AND PURPOSE: The delineation of contrast enhancement in pediatric brain tumors is crucial for effective surgical and treatment planning, as...

GaitNet: Transfer Learning-Enhanced CNN-GRU Architecture for Intention Detection in Healthy and Post-Stroke Participants.

Highly accurate, data-efficient, and real-time detection of human motion intention is essential for the effective control of assistive and rehabilitat...

Evaluating Large Language Models for Post-Publication Promotion: A Blinded Comparative Study of Social Media Posts in Public Health.

BACKGROUND: Social media platforms such as X (formerly Twitter) are increasingly used by journals, authors, and institutions to promote newly publishe...

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...

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...

Low-intensity transcranial ultrasound stimulation improves motor behavior and modulates cortical functional network connectivity in mice with ischemic stroke.

The recovery of motor function in patients with ischemic stroke is closely related to the plastic remodeling of cortical functional networks. Low-inte...

Comprehensive behavioral and demographic correlates in myopia risk among Tibetan adolescents: a machine learning analysis using non‑cycloplegic refraction.

Myopia is a global public health concern, especially among children and adolescents. This exploratory study aimed to develop a myopia risk prediction ...