Surgery

Dual swin transformer for assisting in the diagnosis and surgical prediction of necrotizing enterocolitis.

BACKGROUND: The diagnosis and surgical prediction of necrotizing enterocolitis (NEC) remain challenging. Our goal is to develop an interpretable multimodal artificial intelligence model to assist these key clinical decisions. METHODS: This retrospective study included 484 neonates (242 with NEC, 242 without NEC). We developed a dual Swin Transformer integrating abdominal X-rays (2D branch) and lab...

Integrating multisequence radiomics and clinical features to predict seizure recurrence after gross total resection of pediatric low-grade epilepsy-associated brain tumors.

OBJECTIVE: This study aimed to develop a predictive model integrating clinical features and multisequence MRI radiomics to forecast postoperative seizure outcomes in pediatric patients with low-grade epilepsy-associated tumors (LEATs) who underwent gross total resection (GTR). METHODS: In this study, we propose a novel radiomics-based approach to predict seizure recurrence. The model was further o...

Implementation of the EPIC Deterioration Index Tool on a Medical/Surgical Unit.

PURPOSE: Early detection is crucial for preventing clinical deterioration. This quality improvement project aimed to investigate the application of a ...

Deep learning-based automated assessment of pulmonary artery indices and surgical approach triage for tetralogy of Fallot from multicenter cardiac computed tomography (CT).

AIM: To develop an automated framework for deep learning (DL)-based segmentation, pulmonary artery indices (PAIs) computation, and rule-based surgical...

Accuracy of machine learning in predicting recurrence risk of lumbar disc herniation after percutaneous endoscopic lumbar discectomy: a systematic review and meta-analysis.

BACKGROUND: Lumbar disc herniation (LDH) is a major cause of low back pain and radicular leg pain. Percutaneous endoscopic lumbar discectomy (PELD) is...

Counting papers or counting patients? Reframing surgical excellence in low-and middle-income countries.

The increasing reliance on bibliometric indicators to define academic success has reshaped surgical training and evaluation worldwide. While research ...

Radiologist Perceptions of an AI Tool for Intracranial Hemorrhage Detection in Teleradiology: Cross-Sectional Survey Study.

BACKGROUND: Artificial intelligence (AI) detection tools for intracranial hemorrhage (ICH) are increasingly integrated into radiology workflows. In re...

AI-derived versus surgeon-performed instrumentation in adolescent idiopathic scoliosis: a biomechanical simulation analysis.

PURPOSE: Posterior spinal fusion (PSF) with pedicle screws is the standard treatment for adolescent idiopathic scoliosis (AIS) with curves > 45°, yet ...

Machine learning-assisted self-powered ear tag for animal welfare.

Metabolic health serves as a crucial indicator of animal welfare, yet nutritional imbalances in intensive farming diets often induce metabolic dysregu...

Hepatic Vessel Map (HVM): An Expert-Annotated CT Dataset for Clinically Applicable AI in Liver Vascular Segmentation and Surgical Planning.

Precise delineation of hepatic and portal venous anatomy is crucial for the diagnosis of liver disease, surgical planning, and prognosis prediction. C...

A machine-learning approach to predict additional treatment after Bacillus Calmette-Guérin induction in non-muscle-invasive bladder cancer.

Non-muscle invasive bladder cancer (NMIBC) comprises ~ 75% of newly diagnosed bladder cancer, with high-risk NMIBC associated with high rates of recur...

[Diagnosis and treatment for congenital ossicular chain malformations: current status, challenges, and future prospects].

Congenital ossicular chain malformation constitutes a pivotal etiological factor in conductive hearing loss. Despite its relatively low clinical incid...

Assessing Military Artificial Intelligence Responses to Anterior Cruciate Ligament Injury Frequently Asked Questions.

INTRODUCTION: Artificial intelligence is gaining significant traction, particularly in the orthopedic literature. To date, there has been no published...

Transcriptome sequencing combined with experimental verification to explore potential key genes related to uric acid in diabetic retinopathy.

PURPOSE: Diabetic retinopathy (DR), a major microvascular complication of diabetes and leading global blindness cause, involves uric acid (UA) in its ...

Development and Implementation of a Machine Learning Model for Prediction of Surgical Case Duration.

BACKGROUND: Accurate surgical case duration estimation (CDE) is critical for operating room efficiency, staffing, resource allocation, and patient saf...

Risk stratification for postoperative hematoma following anterior cervical spine surgery: a machine learning approach.

BACKGROUND: Anterior approaches to the cervical spine have consistently increased annually, with commonly performed procedures demonstrating low morbi...

A two-step deep learning framework for predicting difficult video laryngoscopy from ultrasound images: a prospective cohort study.

BACKGROUND: Deep learning integrated with ultrasound systems may assist in predicting difficult airway, a life-threatening complication in anesthesia....

[A nomogram combining ultrasound radiomics and clinical features for predicting pathological invasiveness of papillary thyroid carcinoma].

OBJECTIVES: To develop a nomogram model combining ultrasound radiomics and clinical features and to evaluate its predictive value for pathological inv...

Development and validation of a machine learning model predicting post-tonsillectomy hemorrhage.

PURPOSE: To develop and validate machine learning models to predict post-tonsillectomy hemorrhage. METHODS: This was a machine learning analysis of a ...

Evaluation of Text- and Image-Based Generative Artificial Intelligence for Simulating Impacted Third Molar Extraction.

BACKGROUND: Recent advances in generative artificial intelligence (AI) have opened new opportunities in procedural simulation. However, its clinical a...