Refractive Surgery

HeLP-BAG score: a novel data-driven scoring system for predicting post-operative 30-day mortality using 24-hour preoperative data.

BACKGROUND: Accurate preoperative risk stratification remains challenging, as existing scoring systems are often complex, invasive, or limited to specific patient populations. We aimed to develop a simple, interpretable, and broadly applicable risk score to screen for 30-day postoperative mortality using routinely available variables. METHODS: We developed the HeLP-BAG score using three large surg...

MIRAGE: a multimodal deep learning framework for interpretable risk assessment of high myopia from genetic and retinal imaging data.

High myopia (HM) is a complex condition influenced by both genetic and environmental factors, yet its early prediction and clinical intervention remain challenging due to heterogeneous progression patterns. To support early identification of individuals at risk for HM, we developed MIRAGE, a deep learning framework combining exome-wide genotypes with fundus images for personalized prediction. The ...

Vitabel: A Python Framework for Visualizing and Labelling High-Resolution Physiological Data for Critical Care Machine Learning.

Artificial intelligence offers great opportunities in critical care, particularly when a vast amount of continuously acquired physiological data is in...

Low Rates of Fibrosis in Eyes Treated with the Port Delivery Platform with Ranibizumab or with Monthly Ranibizumab in the Archway Trial.

PURPOSE: To evaluate the proportion of eyes with fibrosis in the Archway neovascular age-related macular degeneration trial, which compared efficacy a...

Preliminary evaluation of an AI-enhanced simulated interprofessional learning intervention for pharmacy students: a mixed-methods pilot study.

BACKGROUND: Interprofessional education (IPE) is vital for preparing pharmacy students for collaborative practice but is often constrained by logistic...

Preoperative hypoalbuminemia predicts early complications, mortality, and discharge disposition following surgical resection of spinal metastases: a nationwide study.

PURPOSE: Patients undergoing surgery for spinal metastases often have limited physiologic reserve. Although hypoalbuminemia is a recognized risk marke...

AI-Based Post-processing for Artefact Mitigation in Radiography: A Systematic Review.

Projectional radiography is vulnerable to artefacts that can impair image quality and obscure or mimic pathology, confounding image interpretation. Th...

Exploring the gut-lung axis in post-liver transplant acute lung injury: A multi-omics approach.

Acute lung injury (ALI) is a significant post-operative complication of liver transplant (LT), with mounting evidence suggesting a role for the gut-lu...

Responsible AI Off-Boarding in Radiology: Staff Perspectives on Decommissioning and a Proposed Withdrawal Framework.

PURPOSE: AI governance commonly emphasises procurement, validation, deployment and performance monitoring but lack guidance on how embedded AI tools s...

An interpretable machine-learning model for post-admission reassessment of bloodstream infection risk in ICU patients with pneumonia.

The aim of this study is to develop and validate a machine learning-based predictive model to assess the risk of acquired bloodstream infection (BSI) ...

Large language models for generating longitudinal synthetic data in low-risk pregnancy care.

This article describes an obstetric dataset covering the full continuum of care of 5000 synthetic low-risk pregnant women, from preconception to post-...

Impact of Availability of Computer-Aided Detection Devices on Adenoma Detection During Colonoscopy: A Cluster Randomized Study.

BACKGROUND AND AIMS: Endoscopists' colonoscopy adenoma detection rates (ADR) are inversely associated with their patients' risk of post-colonoscopy co...

AI-Assisted Medical Documentation in a Multilingual Swiss Health Care System: Proof-of-Concept Study.

BACKGROUND: Medical documentation imposes a significant administrative burden on physicians and reduces time for direct patient care. Artificial intel...

Developing and Testing a Brief Mindfulness Just-in-Time Adaptive Intervention to Reduce Stress Among Caregivers of People With Dementia: Quasi-Experimental Study.

BACKGROUND: Dementia caregiving entails chronic, fluctuating stress with downstream risks to caregivers' mental health and quality of care. Mindfulnes...

A MOOC-based and high-fidelity surgical simulation-integrated clinical internship mode in optometry teaching for ophthalmology graduate students.

BACKGROUND: Clinical medicine postgraduates are expected to attain competencies equivalent to senior resident physicians. However, ophthalmology gradu...

TASC: a time-aware sequence clustering framework with uncertainty quantification for electronic health record trajectories.

Longitudinal electronic health record (EHR) trajectories are highly heterogeneous, sparse, and irregular, making unsupervised temporal pattern discove...

Educational Frameworks for Diagnostic Decision-Making in AI-Enhanced Head and Neck Pathology.

CONTEXT: This paper examines how artificial intelligence (AI) is reshaping diagnostic decision-making in academic and clinical head and neck pathology...

Early Immunological Biomarkers for Personalized Treatment Selection in Severe COVID-19: Post Hoc Machine Learning Analysis of a Randomized Clinical Trial.

BACKGROUND: Severe COVID-19 is a global health concern despite continuous vaccination campaigns because current therapies, such as dexamethasone and r...

AI-based oculomics for trajectory-driven risk stratification of pathologic myopia in paediatric high myopia.

AIMS: To identify early oculomic biomarkers predictive of pathologic myopia (PM) in children with high myopia (HM) and to develop an artificial intell...

Age- and Myopia-Related 3-Dimensional Remodeling of Choroidal Thickness in Children.

PURPOSE: To investigate the 3-dimensional (3D) topographic remodeling patterns of choroidal thickness (ChT) in the macular region in children, and the...