ArticleiScience2025
Explainable-enhanced AI for diagnosing coronary microvascular dysfunction with multimodal imaging.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study develops a clinically applicable, explainability-enhanced AI model, CMVD_MDAS, to improve the diagnosis of coronary microvascular Dysfunction (CMVD) by integrating deep learning and multimodal machine learning. The model was trained and internally validated on 592 myocardial segments and externally validated on 352 clinical patient segments. The architecture includes automated myocardial segmentation, convolutional neural network-based feature extraction, and multimodal diagnostic modeling with SHAP-based feature ranking to enhance explainability. The CMVD_MDAS demonstrated excellent internal performance (AUC: 0.999) and robust external performance (AUC: 0.79), surpassing physician assessments and commercial software. This explainability-enhanced AI solution significantly improves the accuracy and efficiency of CMVD assessment, reducing diagnostic time by approximately 90% and offering a potential diagnostic tool for clinical practice.
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What OpenQuestion holds
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.