Refractive Surgery

Machine learning models for acute kidney injury prediction and management: a scoping review of externally validated studies.

Despite advancements in medical care, acute kidney injury (AKI) remains a major contributor to adverse patient outcomes and presents a significant challenge due to its associated morbidity, mortality, and financial cost. Machine learning (ML) is increasingly being recognized for its potential to transform AKI care by enabling early prediction, detection, and facilitating an individualized approach...

Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma.

Post-hepatectomy liver failure (PHLF) is a severe complication following liver surgery. We aimed to develop a novel, interpretable machine learning (ML) model to predict PHLF. We enrolled 312 hepatocellular carcinoma (HCC) patients who underwent hepatectomy, and 30% of the samples were utilized for internal validation. Variable selection was performed using the least absolute shrinkage and selecti...

Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the US.

BACKGROUND: COVID-19 has been linked to acute kidney injury (AKI) and chronic kidney disease (CKD), but machine learning (ML) models predicting these ...

Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport.

The suspended sediment load (SSL) of a river is a key indicator of water resource management, river morphology, and ecosystem health. This study analy...

Facial surgery preview based on the orthognathic treatment prediction.

BACKGROUND AND OBJECTIVE: Orthognathic surgery consultations are essential for helping patients understand how their facial appearance may change afte...

Post-stroke spontaneous motor recovery in mice can be predicted from acute-phase local field potential using machine learning.

Stroke remains a leading cause of long-term disability, underscoring the urgent need for effective predictors of motor recovery. Understanding the ele...

Construction of a deep learning model and identification of the pivotal characteristics of FGF7- and MGST1- positive fibroblasts in heart failure post-myocardial infarction.

Dysregulation of fibroblast function is closely associated with the occurrence of heart failure after myocardial infarction (post-MI HF). Myocardial f...

Artificial intelligence in the diagnosis and management of refractive errors.

Refractive error is among the leading causes of visual impairment globally. The diagnosis and management of refractive error has traditionally relied ...

Comparative performance analysis of global and chinese-domain large language models for myopia.

BACKGROUND: The performance of global large language models (LLMs), trained largely on Western data, for disease in other settings and languages is un...

Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults.

Adults with opioid use disorder (OUD) are at increased risk for opioid-related complications and repeated hospital admissions. Routine screening for p...

Emerging Models of Care Using IT in Long-Term/Post-Acute Care: A Comparative Analysis of Human and AI-Driven Qualitative Insights.

PURPOSE: As the global population ages, long-term/post-acute care (LTPAC) systems face challenges in ensuring quality care for older adults with compl...

Circulating microRNA profiles are associated with acute pain and stress in castrated and tail docked lambs.

Maintaining animal welfare is an essential component of animal production systems. However, multiple measurements are required to inform an animal's w...

Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.

INTRODUCTION: Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential to prevent postoperative complications and optimize...

Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models.

Orthognathic surgery, or corrective jaw surgery, is performed to correct severe dentofacial deformities and is increasingly sought for cosmetic purpos...

Risk of bias assessment of post-stroke mortality machine learning predictive models: Systematic review.

BACKGROUND: Stroke is a major cause of mortality and permanent disability worldwide. Precise prediction of post-stroke mortality is essential for guid...

Querying the capability of the post-HoLEP endoscopic aspect of the membranous urethral mucosa in predicting urinary incontinence: a prospective AI-based analysis.

INTRODUCTION: Transient stress urinary incontinence (SUI) after holmium laser enucleation of prostate (HoLEP) is commonly linked to intraoperative inj...

How Can Robotic Devices Help Clinicians Determine the Treatment Dose for Post-Stroke Arm Paresis?

Upper limb training dose after stroke is usually quantified by time and repetitions. This study analyzed upper limb motor training dose in stroke part...

Deep learning for hepatocellular carcinoma recurrence before and after liver transplantation: a multicenter cohort study.

Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) is a major contributor to mortality. We developed a recurrence prediction s...

Automated Detection of Keratorefractive Laser Surgeries on Optical Coherence Tomography Using Deep Learning.

PURPOSE: To report a deep learning neural network on anterior segment optical coherence tomography (AS-OCT) for automated detection of different kerat...

Machine Learning in Small-Molecule Mass Spectrometry.

Tandem mass spectrometry (MS/MS) is crucial for small-molecule analysis; however, traditional computational methods are limited by incomplete referenc...