Surgery

Physiology-informed machine learning for patient-level surgical decision-making in severe traumatic brain injury: Multicenter model development with external validation.

BACKGROUND: Surgical decisions for severe traumatic brain injury (TBI) are often made under prognostic uncertainty. Existing prognostic models predict overall outcomes but do not estimate how an individual's outcome might change with the decision to operate. Moreover, physiologic data such as vital signs are often collected but rarely used for such decision support. We aimed to develop and externa...

Acute spinal cord injury: contemporary management and novel therapies.

Acute spinal cord injury (SCI) remains a life-threatening condition associated with substantial morbidity, mortality, and long-term disability. Despite the advances in trauma systems, surgical techniques, and critical care, the optimal management strategies for acute SCI continue to improve, and clinical practice remains heterogeneous across institutions. The pathophysiology of acute SCI involves ...

CEREBLEED: Automated Quantification and Severity Scoring of Intracranial Hemorrhage on Noncontrast CT.

BACKGROUND AND OBJECTIVES: Standardized interpretation of intracranial hemorrhage (ICH) severity on noncontrast computed tomography (NCCT) is limited ...

A High-Quality Endoscopic Image Dataset with Annotated Recurrent Laryngeal Nerve for AI-Assisted Thyroid Surgery.

The integration of artificial intelligence (AI) into surgical navigation represents a pivotal advancement in modern operative medicine. In endoscopic ...

Machine learning-based prediction model for risk factors associated with conversion to open surgery in pediatric laparoscopic radical resection of choledochal cysts.

Prospectively screening for venous thromboembolism in patients with esophagectomy for cancer improves survival: The complexity of simplicity.

BACKGROUND: We implemented a prospective screening program to detect deep venous thromboembolism and define its prevalence in patients with esophageal...

Beyond block time: a head-to-head comparison of reinforcement learning, genetic algorithms, and predict-then-optimize scheduling for operating room workflow using discrete-event simulation.

BACKGROUND: Operating room (OR) inefficiency persists despite decades of process improvement, largely due to stochastic case durations, emergency disr...

Progress of Multimodal Imaging: Bridging Orthodontics to Comprehensive Oral Multidisciplinary Care.

INTRODUCTION AND AIMS: For decades, orthodontic diagnosis and treatment have primarily depended on physical models, imaging techniques, and three-dime...

Non-lethal molecular biomonitoring of pyrethroid exposure in freshwater fish using FTIR spectroscopy and machine learning.

Pyrethroid pesticides such as lambda-cyhalothrin (LC) continue to pose substantial risks to freshwater ecosystems, where sublethal exposures may impai...

Deciphering risk factors for severe postherpetic neuralgia in patients with herpes zoster: an interpretable machine learning approach.

INTRODUCTION: Postherpetic neuralgia (PHN) is a common complication of herpes zoster (HZ). This study aimed to use a large real-world electronic medic...

Artificial Intelligence, Connected Care, and Enabling Digital Health Technologies in Rare Diseases With a Focus on Lysosomal Storage Disorders: Scoping Review.

BACKGROUND: Rare diseases affect more than 300 million people globally, and only about 5% have approved therapies. Lysosomal storage disorders (LSDs) ...

Individualized treatment strategies and long-term prognosis of congenital hydrocephalus: an integrated analysis based on multicenter retrospective data and machine learning.

INTRODUCTION: Hydrocephalus is a common pediatric neurological disorder characterized by abnormal head enlargement, intellectual disability, visual im...

Accurate Segmentation of Surgical Instruments via Spectral-Attentive Contextual Interaction Network.

Surgical instrument segmentation is crucial for enhancing visual perception and enabling precise manipulation in robotic surgical systems. However, cu...

Robotic process automation for identifying missing codes on insurance claims.

OBJECTIVES: This study aimed to develop and implement robotic process automation (RPA) for identifying missing codes during insurance claim post-revie...

Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling.

OBJECTIVES: To investigate the association between quantitative retinal vascular parameters and coronary artery disease (CAD) and to evaluate the effi...

Clinical applications of EEG connectivity in acute brain injuries: A systematic review.

OBJECTIVE: While connectivity methods have been widely studied as predictors of recovery in chronic disorders of consciousness (DoC), evidence for EEG...

Benchmarking text encoding strategies in multimodal clinical data for surgical case duration prediction.

BACKGROUND: Operating rooms (ORs) are highly resource-intensive, yet surgical case duration is often estimated using heuristics that are prone to erro...

Exploration of an admittance control method for multi-segment spinal motion loading.

BACKGROUND: In vitro testing is a fundamental approach for advancing spinal biomechanics research. However, existing loading methods still exhibit not...

Construction and validation of a prediction model for textbook outcome in distal cholangiocarcinoma undergoing pancreaticoduodenectomy based on six machine learning algorithms.

BACKGROUND: Textbook outcome (TO) serves as a validated composite metric for short-term surgical quality in pancreaticoduodenectomy (PD) patients. Thi...

SCAR-Net-assisted ultrasound diagnosis of postoperative scars and recurrent lesions in breast cancer.

Postoperative differentiation between scar tissue and recurrent lesions in patients with breast cancer presents a significant diagnostic challenge. Th...