AIMC Topic: Cardiac Surgical Procedures

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SeptalPro: Development and Usability Evaluation of a Force-Sensing Robotic Transseptal Puncture System for Cardiac Interventions.

IEEE transactions on bio-medical engineering
OBJECTIVE: This study aimed to develop and evaluate the SeptalPro system, a novel robotic platform designed to enhance the precision, safety, and efficiency of transseptal puncture (TSP) procedures by integrating real-time remote control and force se...

A Basic Machine Learning Primer for Surgical Research in Congenital Heart Disease.

World journal for pediatric & congenital heart surgery
Artificial intelligence and machine learning are rapidly transforming medicine, healthcare, and surgery. Machine learning is a valuable tool for surgeons and researchers in pediatric cardiovascular and thoracic surgery, with innovative applications c...

Imitation Learning for Path Planning in Cardiac Percutaneous Interventions.

IEEE transactions on bio-medical engineering
OBJECTIVE: Mitral regurgitation is a valvular heart disease particularly affecting the aging population. Minimally invasive transcatheter procedures offer benefits over traditional open-chest surgery but require significant operator skill and hand-ey...

Attention to early stages: predicting acute kidney injury in a post cardiosurgical ICU setting using an inclusive time-to-event model.

Computers in biology and medicine
BACKGROUND: Acute kidney injury (AKI) is a critical complication in intensive care units (ICUs) that is known to have multifaceted impacts. However, as AKI is often detected too late, early prediction is crucial for timely intervention.

A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the...

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 ...

Using machine learning to predict bleeding after cardiac surgery.

European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery
OBJECTIVES: The primary objective was to predict bleeding after cardiac surgery with machine learning using the data from the Australia New Zealand Society of Cardiac and Thoracic Surgeons Cardiac Surgery Database, cardiopulmonary bypass perfusion da...

Artificial intelligence and cardiac surgery risk assessment.

European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery