Evidence map›Paper›PMID 42434354›Full record

ArticleJournal of medical signals and sensors2026

Creation of a Standardized Myocardial Perfusion Single-photon Emission Computed Tomography Database to Enhance Coronary Artery Disease Research.

Abdurrahim Rahimian, Masoud Moslehi, Hossein Rabbani, Mahnaz Etehadtavakol, Tohid Dehghani

Abstract read
In one paragraph

Article in Journal of medical signals and sensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Abdurrahim RahimianMedical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Masoud MoslehiDepartment of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Hossein RabbaniMedical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Mahnaz EtehadtavakolDepartment of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Tohid DehghaniMedical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Coronary artery disease (CAD) remains the leading global cause of cardiovascular mortality. Although single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is widely used, progress in artificial intelligence (AI)-based diagnostic tools is constrained by the limited availability of modern, high-quality, and consistently labeled imaging datasets. Methods: We retrospectively analyzed 144 rest-stress MIBI (99m Tc-methoxy isobutyl isonitrile) SPECT MPI studies acquired using ASNC/EANM-compliant protocols. Images were reconstructed using filtered back projection and independently interpreted by three nuclear cardiology specialists. Clinical, demographic, and imaging variables were analyzed using SPSS v26, with Results: Significant sex-specific differences in CAD presentation were observed. Women with CAD were older and more frequently demonstrated perfusion patterns compatible with microvascular dysfunction, whereas men exhibited larger territorial defects. Diabetes prevalence was significantly higher among CAD-positive patients, whereas smoking patterns differed markedly by sex. Family history of CAD was significantly more common among CAD-positive subjects. Perfusion abnormalities correlated strongly with cumulative cardiovascular risk burden. Interobserver agreement was excellent (Intraclass correlation coefficient (ICC) = 0.87). Conclusions: We introduce a rigorously standardized, clinically annotated SPECT MPI dataset tailored for developing and validating explainable AI models in nuclear cardiology. Its sex-stratified structure supports research into CAD phenotypes and enhances reproducibility. Public Access: https://misp.mui.ac.ir/fa/t2-dual-head-spectct-coronary-artery-disease-cad.

Indexed as

Artificial intelligencecoronary artery diseasedatasetmyocardial perfusionSPECT

Identifiers

PMID42434354
PMCPMC13354128

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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.