Surgery

Integrated imaging-informed and machine learning-based prognostic models for risk stratification in connective tissue disease-associated interstitial lung disease: a multicenter study.

OBJECTIVES: Connective tissue disease-associated interstitial lung disease (CTD-ILD) exhibits heterogeneous clinical outcomes, and traditional ILD-GAP score provides limited prognostic precision. We hypothesized that integration of imaging-derived fibrosis measures would improve prognostic discrimination compared with physiology-based models. We aimed to develop and externally validate integrated ...

Natural Language Processing for Automated Classification of Cleft and Craniofacial Procedures From Operative Notes: Model Development and Feasibility Study.

BACKGROUND: The accurate classification of operative notes is essential for surgical outcomes research; however, CPT code classification is notoriously nonspecific for many procedures. In such situations, the operative note (or "dictation") must be reviewed manually, a process that is labor-intensive and unsustainable. Natural language processing demonstrates tremendous potential for improving the...

Computational neuroelectrophysiology and artificial intelligence for drug-resistant epilepsy: Recent advances, current challenges, and future directions.

Drug-resistant epilepsy (DRE) affects approximately 30% of epilepsy patients, with surgical cure rates below 70%. This challenge drives a fundamental ...

State of the Art: Scapholunate Ligament Injuries.

ABSTRACT: Scapholunate ligament injuries are the most common ligamentous injuries of the wrist and typically result from high-energy trauma, most ofte...

Propensity score-matched multicentric observational cohort study comparing robotic cholecystectomy with laparoscopic cholecystectomy.

BACKGROUND: With increasing penetration of robotic surgery, robotic cholecystectomies are becoming more popular. There are several benefits which may ...

Application of deep learning for surgical decision support during single-incision laparoscopic cholecystectomy.

OBJECTIVE: This study aimed to develop an Artificial intelligence (AI) model based on Mask2Former to identify safe and hazard zones during Single-inci...

Surgeons' perceptions of artificial intelligence (AI) for gaze guidance in laparoscopic cholecystectomy.

BACKGROUND: Artificial intelligence (AI) tools offer new opportunities to support human perception and provide gaze guidance in surgery. However, ther...

Noninvasive neurophysiological diagnostics of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage: a scoping review.

BACKGROUND: Delayed cerebral ischemia (DCI) is a major complication following aneurysmal subarachnoid hemorrhage (aSAH), affecting outcomes. Given its...

Computational Model for Predicting Optimal Clinical Intervention in Pre-Operative Neonates with Transposition of the Great Arteries.

PURPOSE: Transposition of the Great Arteries (TGA) is a congenital heart defect characterized by the abnormal positioning of the pulmonary artery over...

Innovations in Gastric Cancer Surgery During Early Minimally Invasive Era and Future Perspectives.

The landscape of gastric cancer surgery has undergone remarkable transformation, evolving from traditional open procedures to the modern era of live-s...

Near-infrared imaging-based high-content analysis for label-free assessment of internal cellular heterogeneity in spheroid.

Non-invasive analysis of spheroid quality was essential because spheroids better recapitulated in vivo-like cell-cell and cell-extracellular matrix (E...

Dynamic machine learning model integrating resting energy expenditure for predicting postoperative complications after gastrectomy for gastric cancer.

PURPOSE: To investigate whether perioperative resting energy expenditure (REE) dynamics improve prediction of postoperative complications after gastre...

Artificial intelligence model predicts malignant transformation of oral leukoplakia and optimizes interventions.

PURPOSE: Oral leukoplakia (OL), the most common oral potentially malignant disorder (OPMD), poses a significant risk for transformation to oral squamo...

Artificial Intelligence Applications in General Surgery in the United States of America: A Bibliometric Analysis.

BACKGROUND: Artificial intelligence (AI) is rapidly transforming surgical practice, with applications spanning preoperative planning, intraoperative g...

Diagnostic accuracy of machine learning approaches for suicide‑related outcomes: a meta‑analysis.

OBJECTIVE: This diagnostic test accuracy meta-analysis aimed to provide clinically interpretable estimates (sensitivity, specificity, likelihood ratio...

Applications of artificial intelligence along the perioperative pathway in metabolic and bariatric surgery.

BACKGROUND: The use of artificial intelligence (AI) has rapidly increased in metabolic and bariatric surgery (MBS) in recent years, necessitating a co...

Brain-age defines a structural signature of outcomes after vagus nerve stimulation in children.

INTRODUCTION: Outcomes following vagus nerve stimulation (VNS) are difficult to predict prior to surgery in pediatric drug-resistant epilepsy (DRE). W...

Rapid Multi-Parametric Quantitative MRI via Deep Learning-Based Synthetic-to-Real Reconstruction and 3D SSFP-MOLED Imaging.

Multi-parametric quantitative magnetic resonance imaging (mqMRI) holds significant clinical potential through multi-parametric tissue characterization...

A Novel Wearables-Based Sleep Quality Index to Quantify Postoperative Sleep Quality.

OBJECTIVE: To develop a novel wearables-based sleep index that can quantify sleep disruption after lung resection and to apply machine learning analys...

Accuracy of Fluoroscopic Navigation for Predicting Leg Length and Offset in Primary Hip Arthroplasty.

BACKGROUND: Fluoroscopic-assisted computer navigation is widely used to guide intraoperative decisions on leg length (LL) and offset in primary total ...