ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2020
Diagnostic performance of an artificial intelligence-driven cardiac-structured reporting system for myocardial perfusion SPECT imaging.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 38 citations in OpenAlex.
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- Artificial Intelligence in Nuclear Cardiology.Journal of clinical medicine · 2025Review
- Diagnostic report generation for macular diseases by natural language processing algorithms.The British journal of ophthalmology · 2025Article
- Artificial Intelligence in Nuclear Cardiac Imaging: Novel Advances, Emerging Techniques, and Recent Clinical Trials.Journal of clinical medicine · 2025Review
- Advancements in artificial intelligence-driven techniques for interventional cardiology.Cardiology journal · 2024Review
- Quantitation of diffuse myocardial ischemia with mental stress and its association with cardiovascular events in individuals with recent myocardial infarction.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Diagnosis of diffuse ischemia with SPECT relative perfusion imaging: How to eat soup with a fork?Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Integrating artificial intelligence and natural language processing for computer-assisted reporting and report understanding in nuclear cardiology.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Review
- Preparing for the Artificial Intelligence Revolution in Nuclear Cardiology.Nuclear medicine and molecular imaging · 2023Review
- "Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation inJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- Artificial intelligence in nuclear cardiology: Preparing for the fifth industrial revolution.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Article
- Early onset of left ventricular regional asynchrony in arteries with sub-clinical stenosis.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Article
- A review of the application of machine learning in molecular imaging.Annals of translational medicine · 2021Review
- Quantitative clinical nuclear cardiology, part 2: Evolving/emerging applications.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Review
- Physician centred imaging interpretation is dying out - why should I be a nuclear medicine physician?European journal of nuclear medicine and molecular imaging · 2019Review
- Leveraging latest computer science tools to advance nuclear cardiology.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2019Article
- Artificial Intelligence in Cardiovascular Imaging: Current Landscape, Clinical Impact, and Future Directions.Discoveries (Craiova, Romania)Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesTo describe and validate an artificial intelligence (AI)-driven structured reporting system by direct comparison of automatically generated reports to results from actual clinical reports generated by nuclear cardiology experts.
backgroundQuantitative parameters extracted from myocardial perfusion imaging (MPI) studies are used by our AI reporting system to generate automatically a guideline-compliant structured report (sR).
methodA new nonparametric approach generates distribution functions of rest and stress, perfusion, and thickening, for each of 17 left ventricle segments that are then transformed to certainty factors (CFs) that a segment is hypoperfused, ischemic. These CFs are then input to our set of heuristic rules used to reach diagnostic findings and impressions propagated into a sR referred as an AI-driven structured report (AIsR). The diagnostic accuracy of the AIsR for detecting coronary artery disease (CAD) and ischemia was tested in 1,000 patients who had undergone rest/stress SPECT MPI.
resultsAt the high-specificity (SP) level, in a subset of 100 patients, there were no statistical differences in the agreements between the AIsr, and nine experts' impressions of CAD (P = .33) or ischemia (P = .37). This high-SP level also yielded the highest accuracy across global and regional results in the 1,000 patients. These accuracies were statistically significantly better than the other two levels [sensitivity (SN)/SP tradeoff, high SN] across all comparisons.
conclusionsThis AI reporting system automatically generates a structured natural language report with a diagnostic performance comparable to those of experts.
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