Surgery

Machine Learning Model for Predicting Visual Acuity Improvement After Intrastromal Corneal Ring Surgery in Patients With Keratoconus.

BACKGROUND: Keratoconus is a progressive, degenerative corneal disease that can lead to significant ...

Super Learner Enhances Postoperative Complication Prediction in Colorectal Surgery.

OBJECTIVE: To determine if a Super Learner (SL) machine learning approach could improve the predicti...

Machine learning-driven inverse design of puncture needles with tailored mechanics.

BACKGROUND: In minimally invasive surgery, designing puncture needles with customizable structures t...

Developing an artificial intelligence model for phase recognition in robot-assisted radical prostatectomy.

OBJECTIVES: To develop and evaluate a convolutional neural network (CNN)-based model for recognising...

Minimally invasive surgery: a historical and legal perspective on technological transformation.

Minimally Invasive Surgery (MIS) has experienced a significant evolution over the last 5,000 years, ...

Development and validation of a nomogram model to predict postoperative delirium after resection of esophageal cancer.

The study aimed to establish and validate a nomogram model to predict postoperative delirium (POD) a...

The Impact of ChatGPT in Plastic Reconstructive Surgery: A Focus on Head and Neck Abnormalities.

Artificial Intelligence (AI), particularly ChatGPT, is reshaping how medical knowledge is accessed a...

The role of universal markers in machine learning-derived generalisable peri-operative risk prediction.

Artificial intelligence in radiology: diagnostic sensitivity of ChatGPT for detecting hemorrhages in cranial computed tomography scans.

PURPOSE: Chat Generative Pre-trained Transformer (ChatGPT)-4V, a large language model developed by O...

A preliminary exploration of surgical strategies for solitary papillary thyroid carcinoma on the isthmus.

BACKGROUND: For solitary papillary thyroid carcinoma on the isthmic (SPTCI), there are currently no ...

Comparison Between Cox Proportional Hazards and Machine Learning Models for the Prognostication of Recurrence and Survival Following Liver Resection for Hepatocellular Carcinoma.

BACKGROUND: A robust prognostication model after liver resection for hepatocellular carcinoma (HCC) ...

Emerging innovations in ophthalmic drug delivery for diabetic retinopathy: a translational perspective.

Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes and a leading caus...

The AI Surge in Surgery: Evolution, Challenges, and Ethical Considerations.

Interestingly, the integration of AI technologies into surgery in the past 5 years has increased by ...

Visualized load-bearing-based robot control system improves early function in femoral neck fracture patients: a randomized controlled trial focusing on outcomes after total hip arthroplasty.

BACKGROUND: After total hip arthroplasty (THA), standing and walking balance are greatly affected in...

Collaborative assessment of the risk of postoperative progression in early-stage non-small cell lung cancer: a robust federated learning model.

BACKGROUND: While the TNM staging system provides valuable insights into the extent of disease, pred...

Development of an artificial intelligence algorithm for automated surgical gestures annotation.

Surgical gestures analysis is a promising method to assess surgical procedure quality, but manual an...

Simulation-based training in minimally invasive surgical therapies (MIST): current evidence and future directions for artificial intelligence integration-a systematic review by EAU endourology.

INTRODUCTION: Benign prostatic hyperplasia (BPH) affects a growing proportion of the aging male popu...

Real-time color flow mapping of ultrasound microrobots.

Visualization and tracking of microrobots in real time pose key challenges for surgical microrobotic...

AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients.

With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and...

Establishment of an interpretable MRI radiomics-based machine learning model capable of predicting axillary lymph node metastasis in invasive breast cancer.

This study sought to develop a radiomics model capable of predicting axillary lymph node metastasis ...

Development of a machine learning-derived model to predict unplanned ICU admissions after major non-cardiac surgery.

BACKGROUND: Unplanned postoperative intensive care unit admissions (UIAs) are rare events that cause...