International Perfusion Association

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Category: Artificial Intelligence

ML ECMO

Utilizing Machine Learning to Predict Neurological Injury in Venovenous Extracorporeal Membrane Oxygenation Patients: An Extracorporeal Life Support Organization Registry Analysis

In a study analyzing 37,473 VV-ECMO patients, machine learning was used to predict acute brain injury (ABI), revealing a 7.1% incidence of ABI and identifying pre-ECMO cardiac arrest as the most significant risk factor. The study’s machine learning models, however, showed sub-optimal performance in predicting ABI, attributed to the low prevalence of ABI and the lack of standardized neuromonitoring and imaging protocols in the ELSO Registry data.

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January 12, 2024
AT Heart

A Review of Top Cardiology and Cardiovascular Medicine Journal Guidelines regarding the use of Generative Artificial Intelligence Tools in Scientific Writing

A review of the top 25 cardiology and cardiovascular medicine journals according to the 2023 SCImago rankings revealed that all allow the use of generative AI in scientific writing within ICMJE limitations, but prohibit AI in authorship, image generation, and peer review processes, requiring authors to take full responsibility for their work. These guidelines are standardized and followed by all studied journals, emphasizing the importance of adhering to and updating these policies to uphold scientific integrity.

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January 9, 2024
Photo Safety GPT

Simulated Cardiopulmonary Bypass: A High Fidelity Model for Developing and Accessing Clinical Perfusion Skills

The study describes a simulated cardiopulmonary bypass (CPB) model integrated into a mock operative theater, aiming to enhance the training of novice perfusionists. Results from 81 participants indicated high fidelity and effectiveness of the simulation in replicating real CPB scenarios and skills, suggesting its potential for educational use in clinical perfusion training.

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January 5, 2024
Chip Heart

Validation of an Automated Artificial Intelligence System for 12‑Lead ECG Interpretation

This study evaluates an AI-powered electrocardiogram (ECG) system with six deep neural networks (DNNs), trained to detect 20 diagnostic patterns in standard 12-lead ECGs, and compares its performance to current computerized interpretation methods. The results show that the AI system outperformed or matched state-of-the-art computerized ECG interpretation in all diagnostic categories, demonstrating its potential as a reliable clinical tool for detecting electrocardiographic abnormalities.

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January 3, 2024
Chat GPT Image

Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study

This study compares medical texts written by human experts with those generated by ChatGPT, revealing that while ChatGPT’s texts are fluent, they lack the specific, useful information typically found in human-authored texts. It also presents a machine learning method, with over 95% accuracy, for distinguishing between these two types of texts, underscoring the importance of responsible AI use in medicine.

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December 30, 2023

Supervised Machine Learning Model to Predict Mortality in Patients Undergoing Venovenous Extracorporeal Membrane Oxygenation from a Nationwide Multicentre Registry

Machine learning models, specifically extreme gradient boosting and light gradient boosting, demonstrated higher accuracy than conventional models in predicting 90-day mortality for patients undergoing venovenous extracorporeal membrane oxygenation (VV-ECMO). These advanced models, outperforming existing methods like RESP and PRESERVE, show potential for improving patient selection by identifying those less likely to benefit from VV-ECMO therapy.

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December 29, 2023

Artificial Intelligence-Based Analysis of Body Composition Predicts Outcome in Patients Receiving Long-Term Mechanical Circulatory Support

This study used artificial intelligence to analyze preoperative CT scans of heart failure patients receiving left ventricular assist device (LVAD) implantations, revealing that greater adipose tissue areas are associated with higher postoperative complications and in-hospital mortality. It concluded that preoperative body composition, particularly adipose tissue, can predict poorer outcomes post-LVAD implantation, impacting postoperative quality of life and walking distance.

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December 28, 2023

Organizational Factors in Clinical Data Sharing for Artificial Intelligence in Health Care

This qualitative study explored organizational readiness for sharing health data for AI development across various sectors, finding that motivation and capabilities are central to data-sharing efforts. The study highlights that while organizational values align with data-sharing priorities, incentives can further enhance cross-sector collaborations and overcome barriers, suggesting tailored incentives may improve the sustainable flow of health data for AI development.

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December 28, 2023
Machine Learning AKI

Incorporating Intraoperative Blood Pressure Time-Series Variables to Assist in Prediction of Acute Kidney Injury After Type A Acute Aortic Dissection Repair: An Interpretable Machine Learning Model

This study developed an XGBoost machine learning model using intraoperative blood pressure time-series data to predict the risk of acute kidney injury (AKI) after Type A acute aortic dissection repair. The model, which outperformed others in accuracy, identified factors like intraoperative urine output and the duration of mean arterial pressure below 65 mmHg as critical predictors for postoperative AKI.

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December 25, 2023
Chat GTP4 Xmas

Role of Generative Artificial Intelligence in Publishing. What is Acceptable, What is Not

Generative Artificial Intelligence (AI), including platforms like ChatGPT, is increasingly used in the scientific publishing world for tasks ranging from improving the quality of manuscripts to aiding in peer review processes. However, its use raises ethical concerns, such as potential cheating by students, breach of confidentiality by peer reviewers, and the opacity of AI systems, leading to calls for transparency and accountability in the use of AI in scientific publications, as well as guidelines for authors, reviewers, and publishers in declaring AI-generated content.

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December 24, 2023
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