AI Medical Compendium Topic:
Postoperative Complications

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Artificial Intelligence in Predicting Ocular Hypertension After Descemet Membrane Endothelial Keratoplasty.

Investigative ophthalmology & visual science
PURPOSE: Descemet membrane endothelial keratoplasty (DMEK) has emerged as a novel approach in corneal transplantation over the past two decades. This study aims to identify predisposing risk factors for post-DMEK ocular hypertension (OHT) and develop...

AI-delirium guard: Predictive modeling of postoperative delirium in elderly surgical patients.

PloS one
INTRODUCTION: In older patients, postoperative delirium (POD) is a major complication that can result in greater morbidity, longer hospital stays, and higher healthcare expenses. Accurate prediction models for POD can enhance patient outcomes by guid...

Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy.

PloS one
Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperative infection allows a timely administration of appropriate antibiotic treatment. However, doctors ca...

Unveiling the Immune Landscape of Delirium through Single-Cell RNA Sequencing and Machine Learning: Towards Precision Diagnosis and Therapy.

Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society
BACKGROUND: Postoperative delirium (POD) poses significant clinical challenges regarding its diagnosis and treatment. Identifying biomarkers that can predict and diagnose POD is crucial for improving patient outcomes.

Investigating surgeon performance metrics as key predictors of robotic herniorrhaphy outcomes using iterative machine learning models: retrospective study.

BJS open
BACKGROUND: Robotic data streams allow for capture of objective performance indicators, providing the ability to quantify and analyse operator technique and movement in optimizing postoperative outcomes. This study provided proof-of-concept demonstra...

Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward.

World journal of gastroenterology
In a recent paper, Hong developed an artificial intelligence (AI)-driven predictive scoring system for potential complications following laparoscopic radical gastrectomy for gastric cancer patients. They demonstrated that integrating AI with random ...

Development of machine learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma.

Medicine
The aim of this study was to develop a machine-learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma. We included patients who underwent craniotomy and evacuation of hematoma due to traumatic brain inj...

Predicting Unplanned Return to Operating Room Following Primary Total Shoulder Arthroplasty: Insights from Fair and Explainable Ensemble Machine Learning.

Studies in health technology and informatics
Reoperation is the most significant complication following any surgical procedure. Developing machine learning methods that predict the need for reoperation will allow for improved shared surgical decision making and patient-specific and preoperative...

Machine Learning with Clinical and Intraoperative Biosignal Data for Predicting Cardiac Surgery-Associated Acute Kidney Injury.

Studies in health technology and informatics
Early identification of patients at high risk of cardiac surgery-associated acute kidney injury (CSA-AKI) is crucial for its prevention. We aimed to leverage perioperative clinical and intraoperative biosignal data to develop machine learning models ...