AIMC Topic: Postoperative Complications
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Automated diagnosis for extraction difficulty of maxillary and mandibular third molars and post-extraction complications using deep learning.
Scientific reports
May 30, 2025
Optimal surgical methods require accurate prediction of extraction difficulty and complications. Although various automated methods related to third molar (M3) extraction have been proposed, none fully predict both extraction difficulty and post-extr...
Machine Learning for Predicting Postoperative Functional Disability and Mortality Among Older Patients With Cancer: Retrospective Cohort Study.
JMIR aging
May 14, 2025
BACKGROUND: The global cancer burden is rapidly increasing, with 20 million new cases estimated in 2022. The world population aged ≥65 years is also increasing, projected to reach 15.9% by 2050, making cancer control for older patients urgent. Surgic...
Predictors and associations of complications in ureteroscopy for stone disease using AI: outcomes from the FLEXOR registry.
Urolithiasis
May 14, 2025
We aimed to develop machine learning(ML) algorithms to evaluate complications of flexible ureteroscopy and laser lithotripsy(fURSL), providing a valid predictive model. 15 ML algorithms were trained on a large number fURSL data from > 6500 patients f...
Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning Perspective.
Medicina (Kaunas, Lithuania)
May 13, 2025
: Postoperative delirium (POD) is a frequent and severe complication following cardiac surgery, particularly in high-risk patients undergoing coronary artery bypass grafting (CABG) and aortic valve replacement (AVR). Despite extensive research, predi...
Intramedullary Bone Graft Harvest Using Reamer-Irrigator-Aspirator System: A Case Series.
Journal of surgical orthopaedic advances
May 12, 2025
Autogenous iliac crest bone grating has been the gold standard. Recently, intramedullary bone graft harvest using a reamer-irrigator-aspirator (RIA) had been gaining more interest among orthopedic surgeons. Twenty-four RIA bone graft harvesting proce...
Machine-learning approaches for risk prediction in transcatheter aortic valve implantation: Systematic review and meta-analysis.
The Journal of thoracic and cardiovascular surgery
May 1, 2025
OBJECTIVES: With the expanding integration of artificial intelligence (AI) and machine learning (ML) into the structural heart domain, numerous ML models have emerged for the prediction of adverse outcomes after transcatheter aortic valve implantatio...
An APRI+ALBI-Based Multivariable Model as a Preoperative Predictor for Posthepatectomy Liver Failure.
Annals of surgery
May 1, 2025
OBJECTIVE AND BACKGROUND: Clinically significant posthepatectomy liver failure (PHLF B+C) remains the main cause of mortality after major hepatic resection. This study aimed to establish an aspartate aminotransferase to platelet ratio combined with a...
Postoperative Complications
ROC Curve
Predictive Value of Tests
Liver Failure
Aspartate Aminotransferases
Aged
Humans
Area Under Curve
Preoperative Period
Bilirubin
Male
Databases, Factual
Platelet Count
Serum Albumin
Hepatectomy
Middle Aged
Female
Predictive Learning Models
Liver Function Tests
Risk Assessment
Utilizing Artificial Intelligence for Predicting Postoperative Complications in Breast Reduction Surgery: A Comprehensive Retrospective Analysis of Predictive Features and Outcomes.
Aesthetic surgery journal
Apr 16, 2025
BACKGROUND: Breast reduction is a common procedure with growing rates in the United States of America, aimed at alleviating the physical and psychological burdens of macromastia. Despite high success rates, it carries a risk of complications, with in...
Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data.
Clinical transplantation
Apr 1, 2025
BACKGROUND AND AIM: Liver transplant (LT) recipients may succumb to graft-related pathologies, contributing to graft fibrosis (GF). Current methods to diagnose GF are limited, ranging from procedural-related complications to low accuracy. With recent...
Predicting Postoperative Circulatory Complications in Older Patients: A Machine Learning Approach.
Biomedical and environmental sciences : BES
Mar 20, 2025
OBJECTIVE: This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications (PCCs) for older patients.
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