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Diagnostic Performance of Artificial Intelligence Algorithms in Prediction of Acute Coronary Syndrome Based on White Blood Cell Properties

NCT06384846 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 3350

Last updated 2026-06-24

No results posted yet for this study

Summary

The goal of this observational study is to evaluate whether artificial intelligence (AI) algorithms can predict or exclude acute coronary syndrome (ACS) in adults using data generated by routine hematology testing. The main questions the study aims to answer are:

* Can AI algorithms based on white blood cell (WBC) data predict or exclude ACS in subjects with suspected ACS?
* Can erythrocyte (EC) and/or thrombocyte (TC) data, where available, improve or complement WBC-based AI prediction of ACS?
* How does the diagnostic performance of the AI algorithms compare with high-sensitivity cardiac troponin (hs-cTn), and can the combination of AI algorithms and hs-cTn improve diagnostic performance?

Participants will undergo clinical assessment and blood testing as part of usual clinical care. Their previously generated clinical information, hematology data, and hs-cTn results will be used to train and test the AI algorithms. Participation in the study does not determine the indication for coronary angiography or treatment, and no additional study-specific treatments are performed.

Conditions

  • Acute Coronary Syndrome
  • Angina Pectoris
  • NSTEMI - Non-ST Segment Elevation MI
  • STEMI - ST Elevation Myocardial Infarction
  • Unstable Angina (UA)

Sponsors & Collaborators

  • RobotDreams GmbH

    lead INDUSTRY

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-02-01
Primary Completion
2026-07-31
Completion
2026-12-31

Countries

  • Austria

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06384846 on ClinicalTrials.gov