ArticleFrontiers in cardiovascular medicine2023
A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- AI-Powered Detection of Left Ventricular Myocardial Scar from Dual-Sequence CT: Global and Segmental Prediction Validated by CMR.Journal of imaging informatics in medicine · 2026Article
- Rewiring the scar: translational advances in cardiac fibrosis.The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2025Review
- Cardiac preservation usingFrontiers in cardiovascular medicine · 2025Article
- Advancements in Cardiac CT Imaging: The Era of Artificial Intelligence.Echocardiography (Mount Kisco, N.Y.) · 2024Review
- Computer-Assisted Algorithm for Quantification of Fibrosis by Native Cardiac CT: A Pilot Study.Journal of clinical medicine · 2024Article
- Cardiac Fibrosis Automated Diagnosis Based on FibrosisNet Network Using CMR Ischemic Cardiomyopathy.Diagnostics (Basel, Switzerland) · 2024Article
- Multimodality Imaging in Advanced Heart Failure for Diagnosis, Management and Follow-Up: A Comprehensive Review.Journal of clinical medicine · 2023Review
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Authors and funding
12 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Diagnosis of myocardial fibrosis is commonly performed with late gadolinium contrast-enhanced (CE) cardiac magnetic resonance (CMR), which might be contraindicated or unavailable. Coronary computed tomography (CCT) is emerging as an alternative to CMR. We sought to evaluate whether a deep learning (DL) model could allow identification of myocardial fibrosis from routine early CE-CCT images. Methods and results: Fifty consecutive patients with known left ventricular (LV) dysfunction (LVD) underwent both CE-CMR and (early and late) CE-CCT. According to the CE-CMR patterns, patients were classified as ischemic ( Conclusions: DL on early CE-CCT acquisition may allow detection of LV sectors affected with myocardial fibrosis, thus without additional contrast-agent administration or radiational dose. Such tool might reduce the user interaction and visual inspection with benefit in both efforts and time.
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