ArticleThe EPMA journal2023
Artificial intelligence to improve ischemia prediction in Rubidium Positron Emission Tomography-a validation study.
Article in The EPMA journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Temporal trends in pretest probability and normal scan rates in myocardial perfusion imaging: a 24-year experience from a Swiss tertiary referral center (between 2000 and 2024).European heart journal. Cardiovascular Imaging · 2026Article
- [Coronary heart disease-a German paradox : Using AI-based technology to solve a relevant healthcare problem].Herzschrittmachertherapie & Elektrophysiologie · 2026Review
- Article
- Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [Scientific reports · 2024Article
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Authors and funding
16 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patients are referred to functional coronary artery disease (CAD) testing based on their pre-test probability (PTP) to search for myocardial ischemia. The recommended prediction tools incorporate three variables (symptoms, age, sex) and are easy to use, but have a limited diagnostic accuracy. Hence, a substantial proportion of non-invasive functional tests reveal no myocardial ischemia, leading to unnecessary radiation exposure and costs. Therefore, preselection of patients before ischemia testing needs to be improved using a more predictive and personalised approach. Aims: Using multiple variables (symptoms, vitals, ECG, biomarkers), artificial intelligence-based tools can provide a detailed and individualised profile of each patient. This could improve PTP assessment and provide a more personalised diagnostic approach in the framework of predictive, preventive and personalised medicine (PPPM). Methods: Consecutive patients ( Results: Ischemia was present in 37.1%. The MPA model was most accurate to predict ischemia (AUC: 0.758, Conclusion: The MPA model enhanced ischemia testing according to the PPPM framework:The MPA model improved individual prediction of ischemia significantly and could safely exclude ischemia based on readily available variables without advanced testing ("predictive").It reduced the proportion of patients in the intermediate PTP range. Therefore, it could be used as a gatekeeper to prevent patients from further unnecessary downstream testing, radiation exposure and costs ("preventive").Consequently, the MPA model could transform ischemia testing towards a more personalised diagnostic algorithm ("personalised"). Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00341-5.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.