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AI-Driven Cardiac Surgery Future

Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes

April 1, 2026 No Comments

Artificial intelligence (AI) is transforming adult cardiovascular medicine and surgery by enhancing diagnostics, surgical planning, intraoperative precision, and postoperative monitoring. Tools such as AI-ECG, automated imaging, and predictive analytics improve risk stratification and outcomes. While real-world deployments show promise, challenges including data quality, bias, and limited prospective validation remain barriers to widespread adoption.

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AI-Driven Kidney Risk Prediction During Cardiac Surgery

Predicting Kidney Injury After Cardiac Surgery With Cardiopulmonary Bypass Using Machine Learning

March 22, 2026 No Comments

This study evaluates a machine learning (ML) model using electronic health record (EHR) data to predict acute kidney injury (AKI) after cardiac surgery. In 130 patients, the AI achieved strong predictive performance (AUROC 0.79 for AKI, 0.83 for 30-day kidney disease). The model enables early, automated risk stratification, offering a practical tool for proactive perioperative management and improved patient outcomes.

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Cybersecurity as it relates to perfusion

Cybersecurity as It Relates to Perfusion

March 21, 2026 No Comments

Cybersecurity is increasingly critical in perfusion as connected medical devices expand vulnerability within hospital networks. Perfusionists must understand risks associated with the Internet of Medical Things (IoMT), adopt strong digital practices, and collaborate with IT teams. The article highlights downtime preparedness, device security awareness, and standardized guidelines to protect patient data and maintain safe clinical operations.

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Rethinking Cardiopulmonary Bypass Management

Rethinking Cardiopulmonary Bypass Management in the Digital Health Era

March 7, 2026 No Comments

Minimally invasive and robotic cardiac surgery reduce surgical trauma and speed recovery but often require longer cardiopulmonary bypass (CPB) times, increasing risks such as renal injury, neurological complications, and systemic inflammation. This review explores how digital health tools—including continuous physiologic monitoring, machine learning analytics, and digital twin simulations—can transform CPB from a static procedural metric into a dynamically optimized variable, enabling personalized perfusion strategies that improve safety and outcomes in modern cardiac surgery.

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AI-Powered ECMO Digital Twin in Virtual Reality Training

Building an Extracorporeal Membrane Oxygenation Digital Twin Using High-Resolution Patient Data: An Artificial Intelligence Model for Virtual Reality Simulation 

February 22, 2026 No Comments

In this multicentre study of 335 ECMO patients, high-resolution device and electronic health record data were integrated to develop a two-stage artificial intelligence model capable of simulating ECMO circuit behavior and patient physiological responses. The digital twin was deployed in a virtual reality platform with real-time inference. Expert evaluation confirmed clinically coherent responses, supporting scalable, high-fidelity ECMO training without dedicated hardware.

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Revolutionizing Cardiovascular Interventions With Artificial Intelligence

May 5, 2025 No Comments

This editorial outlines how artificial intelligence (AI) is transforming cardiovascular interventions by enhancing procedural planning, democratizing medical expertise, and expediting device development. AI-driven tools improve diagnostic accuracy, support training via simulation, and enable the creation of digital twins for personalized treatment planning. The article emphasizes AI’s potential to foster global equity in healthcare access and improve patient outcomes.

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Impact of Inflammation After Cardiac Surgery

Impact of Inflammation After Cardiac Surgery on 30-Day Mortality and Machine Learning Risk Prediction

April 5, 2025 No Comments

This study investigates the effects of systemic inflammatory response syndrome (SIRS) on 30-day mortality following cardiac surgery and develops machine learning models to predict SIRS. Analyzing data from 1,908 patients, researchers found SIRS significantly raised mortality risk. Key predictors included preoperative anemia and intraoperative lactate peaks. Predictive models using random forest achieved high accuracy, offering insights for tailored interventions.

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Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure

Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure: A Retrospective Analysis Based on the MIMIC-IV Database

April 3, 2025 No Comments

This study developed machine learning (ML) models to predict in-hospital mortality among ICU patients with acute heart failure (AHF) using data from the MIMIC-IV database. Among five tested algorithms, XGBoost showed the highest predictive accuracy (AUC: 0.82) and outperformed traditional clinical scoring systems. The model incorporated 18 clinical variables from the first 24 hours of ICU admission to aid early intervention strategies.

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