Surgery

Ten Machine Learning Models for Predicting Preoperative and Postoperative Coagulopathy in Patients With Trauma: Multicenter Cohort Study.

BACKGROUND: Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trauma. ML can enhance predictions and identify which factors contribute the most to posttraumatic mortality. However, no studies have explored the risk factors, complications, and risk prediction of preoperative and postoperative traumatic coagulopathy ...

Death risk prediction model for patients with non-traumatic intracerebral hemorrhage.

BACKGROUND: This study aimed to assess the risk of death from non-traumatic intracerebral hemorrhage (ICH) using a machine learning model.

Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment.

Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are among the most challenging malignancies due to the...

Closing the loop: establishing an autonomous test-learn cycle to optimize induction of bacterial systems using a robotic platform.

One goal of synthetic biology is to provide well-characterised biological parts that behave predictably in genetic assemblies. To achieve this, each p...

Endovascular robotics: technical advances and future directions.

Endovascular interventions excel in treating cardiovascular diseases in a minimally invasive manner, showing improved outcomes over open techniques. H...

Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy.

OBJECTIVE: To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postop...

Development of an artificial intelligence system to indicate intraoperative findings of scarring in laparoscopic cholecystectomy for cholecystitis.

BACKGROUND: The surgical difficulty of laparoscopic cholecystectomy (LC) for acute cholecystitis (AC) and the risk of bile duct injury (BDI) depend on...

Artificial intelligence-based, non-invasive assessment of the central aortic pressure in adults after operative or interventional treatment of aortic coarctation.

BACKGROUND: Aortic coarctation (CoA) is a congenital anomaly leading to upper-body hypertension and lower-body hypotension. Despite surgical or interv...

Predicting postoperative adhesive small bowel obstruction in infants under 3 months with intestinal malrotation: a random forest approach.

OBJECTIVE: This study aimed to develop a predictive model using a random forest algorithm to determine the likelihood of postoperative adhesive small ...

Classification of chicken Eimeria species through deep transfer learning models: A comparative study on model efficacy.

Eimeria is a protozoan parasite that causes coccidiosis in various animal species, especially in chickens, resulting in infections characterized by in...

Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis.

BACKGROUND: Delayed cerebral ischemia (DCI) is a primary contributor to death after subarachnoid hemorrhage (SAH), with significant incidence. Therefo...

Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules.

Percutaneous transthoracic puncture of small pulmonary nodules is technically challenging. We developed a novel electromagnetic navigation puncture sy...

Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection.

Revascularization therapies, such as percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG), alleviate symptoms and treat...

Towards a decision support system for post bariatric hypoglycaemia: development of forecasting algorithms in unrestricted daily-life conditions.

BACKGROUND: Post bariatric hypoglycaemic (PBH) is a late complication of weight loss surgery, characterised by critically low blood glucose levels fol...

Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage.

BACKGROUND: Aneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with significant mortality rates and complex rehabilitation...

Untargeted Swab Touch Spray-Mass Spectrometry Analysis with Machine Learning for On-Site Breast Surgical Margin Assessment.

Direct sampling mass spectrometry (MS) has rapidly advanced with the development of ambient ionization MS techniques. Swab touch-spray (TS)-MS has sho...

Deep learning in surgical process modeling: A systematic review of workflow recognition.

OBJECTIVE: The application of artificial intelligence (AI) in health care has led to a surge of interest in surgical process modeling (SPM). The objec...

Deep Learning-Derived Quantitative Scores for Chronic Rhinosinusitis Assessment: Correlation With Quality of Life Outcomes.

BackgroundComputed tomography (CT) plays a crucial role in assessing chronic rhinosinusitis, but lacks objective quantifiable indicators.ObjectiveThis...

Task-oriented robotic rehabilitation for back mobility and functioning in a post-intensive care unit obese patient: A case report.

BackgroundIntensive care unit (ICU) acquired weakness is a detrimental condition characterized by muscle weakness, difficulty in weaning from mechanic...

Machine learning to predict periprosthetic joint infections following primary total hip arthroplasty using a national database.

INTRODUCTION: Periprosthetic joint infection (PJI) following total hip arthroplasty (THA) remains a devastating complication for patients and surgeons...