Surgery

Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery.

BACKGROUND AND OBJECTIVE: Accurate prediction of perioperative major adverse cardiovascular events (...

Deep learning-assistance significantly increases the detection sensitivity of neurosurgery residents for intracranial aneurysms in subarachnoid hemorrhage.

OBJECTIVE: The purpose of this study was to evaluate the effectiveness of a deep learning model (DLM...

A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study.

OBJECTIVE: Preoperative prediction of visual recovery after pituitary adenoma resection surgery rema...

Prediction of postoperative stroke in patients experienced coronary artery bypass grafting surgery: a machine learning approach.

BACKGROUND: Coronary artery bypass grafting (CABG) surgery has been a widely accepted method for tre...

Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot study.

OBJECTIVES: Similarities between acute ischemic and hemorrhagic stroke make diagnosis and triage cha...

Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models.

BACKGROUND: Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, with hemorrhagi...

Systematic evaluation of machine learning models for postoperative surgical site infection prediction.

BACKGROUND: Surgical site infections (SSIs) lead to increased mortality and morbidity, as well as in...

Artificial intelligence in surgical pathology - Where do we stand, where do we go?

Surgical and neuropathologists continuously search for new and disease-specific features, such as in...

Stress testing deep learning models for prostate cancer detection on biopsies and surgical specimens.

The presence, location, and extent of prostate cancer is assessed by pathologists using H&E-stained ...

Imaging-guided bioresorbable acoustic hydrogel microrobots.

Micro- and nanorobots excel in navigating the intricate and often inaccessible areas of the human bo...

Acupuncture and moxibustion as adjunctive therapy for postoperative gastrointestinal dysfunction in gastric cancer: a systematic review and network meta-analysis.

OBJECTIVES: This study aimed to evaluate and compare the efficacy and safety of different acupunctur...

Editorial: The evolution of minimally invasive urologic surgery: innovations, challenges, and opportunities.

Development and validation of interpretable machine learning models for postoperative pneumonia prediction.

BACKGROUND: Postoperative pneumonia, a prevalent form of hospital-acquired pneumonia, poses signific...

Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.

BACKGROUND: Machine learning (ML) techniques are widely employed across various domains to achieve a...

Super-fast and accurate nonlinear foot deformation Prediction using graph neural networks.

Recently, there has been a significant increase in the number of foot diseases, highlighting the imp...

Classification of speech arrests and speech impairments during awake craniotomy: a multi-databases analysis.

PURPOSE: Awake craniotomy presents a unique opportunity to map and preserve critical brain functions...

Wound imaging software and digital platform to assist review of surgical wounds using patient smartphones: The development and evaluation of artificial intelligence (WISDOM AI study).

INTRODUCTION: Surgical patients frequently experience post-operative complications at home. Digital ...

Machine learning-based prognostic prediction and surgical guidance for intrahepatic cholangiocarcinoma.

The prognosis following radical surgery for intrahepatic cholangiocarcinoma (ICC) is poor, and optim...

Prediction of Symptomatic Intracranial Hemorrhage Before Mechanical Thrombectomy Using Machine Learning in Patients with Anterior Circulation Large Vessel Occlusion.

BACKGROUND: Symptomatic intracranial hemorrhage (sICH) after mechanical thrombectomy (MT) is associa...

Machine learning for predicting poor outcomes in aneurysmal subarachnoid hemorrhage: A systematic review and meta-analysis involving 8445 participants.

Early prediction of poor outcomes in patients impacted with aneurysmal subarachnoid hemorrhage (aSAH...

Development and validation of a deep learning algorithm for the classification of the level of surgical difficulty in impacted mandibular third molar surgery.

The aim of this study was to develop and validate a convolutional neural network (CNN) algorithm for...