Diagnostic Performance of Artificial Intelligence Algorithms in Prediction of Acute Coronary Syndrome Based on White Blood Cell Properties (AI-ACS Trial)

What this Trial Is About

Researchers are evaluating the ability of artificial intelligence (AI) algorithms to predict acute coronary syndrome (ACS) using white blood cell data in adults. This observational study compares the accuracy of AI predictions with the standard high-sensitivity cardiac troponin (hs-cTn) blood tests to see which method better diagnoses ACS. The study aims to improve rapid identification of patients at risk by analyzing routine blood tests with AI technology. The study collects white blood cell data from routine blood tests performed at patient admission using specialized hematology analyzers. The AI models are trained and tested in two phases, each lasting up to 36 months, using data from patients with various chest pain conditions, including those with suspected ACS. No new procedures or treatments are administered as part of the study; it relies entirely on previously collected blood test results and patient information. Participants have already undergone blood testing as part of their usual care, and their existing health data is used to develop and validate AI models. The study includes continuous data quality checks, source data verification, and adherence to strict protocols to ensure accuracy. Researchers monitor how well the AI models perform compared to hs-cTn tests in diagnosing ACS. The total participation duration depends on the data collection timeline, with no extra visits or interventions required.

AI Algorithms in Prediction of ACS Based on Leukocyte Properties