Surgery

Predicting Treatment Response After Total Neoadjuvant Therapy for Locally Advanced Rectal Cancer.

OBJECTIVE: To develop a predictive model for pathological complete response (pCR) after total neoadjuvant therapy (TNT) to inform selection for watch-and-wait (W/W). SUMMARY BACKGROUND DATA: Patient selection for W/W after TNT for locally advanced rectal cancer remains challenging. METHODS: An ensemble of tabular foundation models was fine-tuned in adults with clinical stage II or III microsatelli...

Navigating PI-RADS v2.1 in clinical practice: pitfalls, variability, and the supportive role of AI.

Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 has substantially advanced the standardization of prostate MRI acquisition, interpretation, and reporting and has helped establish a common language for MRI-directed diagnostic pathways. Nevertheless, clinically meaningful variability persists across readers and institutions and continues to influence biopsy referral, targeting, and ...

Development and validation of an interpretable machine learning model for predicting postoperative complications after transanal total mesorectal excision: a multicenter study.

OBJECTIVE: This study aimed to develop and externally validate an interpretable machine learning (ML) model for predicting postoperative complications...

Large Language Models and Metabolic Bariatric Surgery: A Pilot Concordance Study Between ChatGPT and Multidisciplinary Team Recommendations.

INTRODUCTION: Operation selection in metabolic surgery is a complex decision making process led by a multidisciplinary team that integrates multiple a...

3D chromatin architecture-related genes orchestrate LUAD evolution and therapy resistance: insights from integrative machine learning and spatial single-cell mapping.

BACKGROUND: Lung adenocarcinoma (LUAD) is a prevalent and lethal malignancy. The three-dimensional (3D) chromatin architecture significantly influence...

Smartphone-Based Proactive Self-Screening for Ocular Surface Malignancies: A Nonrandomized Clinical Trial.

IMPORTANCE: Ocular surface malignancies pose risks to vision and survival yet are frequently misdiagnosed as benign lesions because of their subtle pr...

Advancing Gastrointestinal Cancer Risk Prediction With Patient-Centered Machine Learning: Machine Learning Modeling Study.

BACKGROUND: Gastrointestinal (GI) cancers are a significant health concern in South Korea. Recently, machine learning (ML) models have emerged as powe...

Comparing the Readability and Usability of Patient Education Materials Generated by Different Large Language Models: ChatGPT, Copilot, and Gemini.

INTRODUCTION: Patients with low health literacy face challenges in understanding and navigating surgical care, leading to surgical disparities. The ri...

Electrocardiogram-based short-term risk stratification for acute kidney injury using time-frequency deep learning analysis.

BACKGROUND AND OBJECTIVES: Acute kidney injury (AKI) is a common and clinically important complication in critically ill and perioperative patients. C...

Development, validation, and user-centric evaluation of an interpretable machine learning decision support tool for the preoperative prediction of mild bleeding disorders (MBD-Check): a prospective diagnostic prediction study.

BACKGROUND: Mild bleeding disorders are the most common inherited bleeding disorders, often leading to perioperative haemorrhages. Preoperative screen...

Machine learning (ML) in intraoperative neuromonitoring (IONM): proof of concept.

PURPOSE: Intraoperative neuromonitoring (IONM) improves safety during pediatric spinal deformity surgery by providing real-time neurophysiological ass...

Clinical indicators associated with pericardial effusion in rheumatoid arthritis: a machine learning-based analysis.

BACKGROUND: Pericardial effusion (PE) is a frequent yet underdiagnosed complication of rheumatoid arthritis (RA), with substantial mortality risk. Nev...

Deep learning-based arterial waveform analysis for predicting postoperative cerebrovascular events in pediatric patients with Moyamoya disease.

BACKGROUND: Postoperative cerebrovascular events, including transient ischemic attacks, infarctions, and hemorrhages, remain a significant concern in ...

Body composition phenotyping of obesity in children aged 6-18 years: multi-strategy clustering and interpretable machine learning.

BACKGROUND: Body composition heterogeneity in childhood obesity is not fully captured by BMI, motivating operational phenotyping using non-invasive me...

Clinical Applicability of Artificial Intelligence-Driven Implant Planning and Surgical Guide Design in the Maxillary Esthetic Zone: A Registry-Based Cohort Study.

OBJECTIVES: This study evaluated the accuracy, time efficiency, and workflow consistency of artificial intelligence (AI)-assisted versus human expert ...

A Machine Learning Approach to Determine the Optimal Age for Total Hip Arthroplasty: When Is Risk for Adverse Outcomes Lowest?

INTRODUCTION: With evolving lifestyles and improvements in surgical techniques, the utilization of total hip arthroplasty (THA) is growing across pati...

Comparison of volume- and surface-based magnetic resonance imaging morphometry algorithms in the detection of focal cortical dysplasia.

OBJECTIVE: Detection of focal cortical dysplasia (FCD) remains a major challenge in presurgical epilepsy diagnostics. Magnetic resonance imaging (MRI)...

Development and internal validation of an explainable machine learning model for predicting textbook outcome after free flap reconstruction in oral cancer.

OBJECTIVE: To develop and internally validate an explainable machine learning model for predicting textbook outcome (TO) after free flap reconstructio...

Immersive virtual reality for cognitive rehabilitation in patients who had a stroke: study protocol for a randomised controlled trial.

INTRODUCTION: Poststroke cognitive impairment (PSCI) is a prevalent complication of stroke, characterised by deficits in one or more cognitive domains...

Profiling of extracellular vesicles from primary hepatocytes, organoids, and mash patients identifies cell injury-specific signatures.

Metabolic Dysfunction-Associated Steatohepatitis (MASH) is a severe form of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), traditio...