Surgery

Surgical technology update for ophthalmic surgery: heads-up display, artificial intelligence, instrumentation, and robotics.

PURPOSE OF REVIEW: Rapid advances in surgical visualization, microsurgical instrumentation, artificial intelligence (AI), and robotic assistance are reshaping ophthalmic surgery. This review evaluates current clinical and experimental evidence to determine how these technologies influence surgical efficiency, precision, safety, and patient outcomes. RECENT FINDINGS: Three-dimensional (3D) heads-up...

Ventral hernia repair in emergency settings. A machine learning model to predict post-operative complications.

BACKGROUND: Emergency ventral hernia repair remains a challenging procedure due to patient instability, contaminated surgical fields, and heterogeneity in hernia types and operative techniques. Predicting postoperative complications in this setting is difficult using traditional statistical methods. Machine learning (ML) may offer improved predictive accuracy by recognizing nonlinear patterns amon...

AI-Driven Mapping of Seizure Spread Patterns.

OBJECTIVE: The focus of epilepsy research has largely been on seizure onset; however, physicians typically examine the patterns of seizure spread past...

Update on topography and tomography for refractive surgery.

PURPOSE OF REVIEW: To review and summarize the current literature on recent advances in corneal topography and anterior segment tomography, highlighti...

Lineage Classification of Pituitary Neuroendocrine Tumors From Whole-Slide Images Using Attention-Guided Graph Representation Learning.

Pituitary neuroendocrine tumors (PitNETs) are common sellar neoplasms and represent a major component of routine pituitary pathology. In the 2022 Worl...

Learning where to look: scaling parkland grade prediction from surgical videos.

PURPOSE: The Parkland Grading Scale (PGS) is widely used to quantify operative difficulty in cholecystectomy, with higher grades associated with worse...

Electronics-free soft robotic minitablet for on-demand gastric molecular sensing and diagnostics in vivo.

Real-time biomarker sensing and molecular sampling in the stomach can transform how gastrointestinal disorders are diagnosed and managed-yet integrati...

Deep learning-enabled monitoring of postoperative fracture healing on serial radiographs: a 150-patient study using an enhanced YOLOv11 framework.

OBJECTIVES: To develop and validate a deep learning framework for classifying postoperative time-points as a proxy task for monitoring longitudinal fr...

Predicting endoscopic third ventriculostomy with choroid plexus cauterization success with machine learning: the importance of ventricle size and the irrelevance of etiology.

OBJECTIVE: Endoscopic third ventriculostomy with choroid plexus cauterization (ETV/CPC) has decreased rates of shunt dependence in infants with hydroc...

Age-related differences in surgical outcomes for traumatic central cord syndrome: a multi-institutional causal machine learning analysis.

OBJECTIVE: Traumatic central cord syndrome (TCCS) is the most common incomplete spinal cord injury, yet the optimal management strategy remains contro...

Benchmarking large language models on persian surgical subspecialty board examinations: a comparative study of ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash.

This study evaluated the performance of three large language models, including ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash, on 532 Persian multiple-ch...

The future of robotic surgery in the age of artificial intelligence.

Patient outcomes after robotic surgery vary widely, often reflecting differences in surgical performance. Artificial intelligence (AI) offers new ways...

Couinaud segment-aware deep learning on point clouds for major liver resection planning.

PURPOSE: In this study, we address the problem of automatic liver resection planning for major surgical procedures, including hemi-hepatectomy and ext...

Construction and verification of multimodal model for prognosis in elderly patients with aneurysmal subarachnoid hemorrhage.

OBJECTIVE: This study aimed to construct and validate an individualized prediction model for poor prognosis in elderly patients with Aneurysmal subara...

Machine learning and neural network algorithms for prediction of C5 palsy after posterior surgery for ossification of posterior longitudinal ligament.

PURPOSE: C5 palsy (C5P) is one of the main postoperative complications of ossification of the posterior longitudinal ligament (OPLL). However, an accu...

Machine Learning in Postcardiotomy Shock: Implications for Temporary Mechanical Circulatory Support.

Postcardiotomy shock (PCS) is a distinct form of cardiogenic shock that occurs after cardiac surgery and necessitates rapid decisions regarding tempor...

Opportunistic CT Fatty Muscle Fraction for Outcome Prediction in Patients Undergoing Transcatheter Mitral Valve Edge-to-Edge Repair.

BACKGROUND: Frailty is a risk factor for adverse outcomes in patients undergoing mitral valve transcatheter edge-to-edge repair (M-TEER) and is interr...

Machine Learning-Based Prediction Model for Infectious Complications in Trauma and Its Association With In-Hospital Mortality.

BACKGROUND: Infectious complications, such as sepsis or catheter-related infections, are common and serious sequelae after trauma. Despite their clini...

Deep learning model for noninvasive prediction of Ki-67 expression and prognostic stratification in breast cancer: a multicenter retrospective study.

OBJECTIVE: Ki-67 correlates with prognosis for patients with breast cancer. However, the evaluation of Ki-67expression relies on pathological analysis...

A Novel, Interpretable Machine Learning Model Predicts Furosemide Dosing After Congenital Cardiac Surgery.

Fluid overload is common after neonatal congenital cardiac surgery (CCS) and is frequently managed with continuous furosemide infusions requiring iter...