A Multimodular AI Algorithm for Automated Assessment of Left Ventricular Function in Ischemic Heart Disease: Ejection Fraction, Wall Motion, and Regional Myocardial Segmentation

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dc.contributor.authorGul, Sidem
dc.contributor.authorTasdemir, Resit
dc.contributor.authorAcikgoz, Beyza
dc.contributor.authorDuman, Hakan
dc.contributor.authorHodzic, Hamza
dc.contributor.authorKoker, Sena
dc.contributor.authorKivrak, Mehmet
dc.date.accessioned2026-06-19T06:37:33Z
dc.date.available2026-06-19T06:37:33Z
dc.date.issued2026
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractBackground: Ischemic heart damage reduces the pumping efficiency of the heart by affecting the left ventricular ejection fraction (LVEF) and causing wall motion abnormality (WMA). In daily clinical practice, these parameters are interpreted by physicians using two dimensional transthoracic echocardiography (2D-TTE). Because 2D-TTE reports rely on visual evaluations, they are subject to experience-based limitations and exhibit low reproducibility. Aims: To develop an artificial intelligence algorithm composed of two modules that enable automatic LVEF calculation and WMA detection for analyzing 2D-TTE images. Study Design: Diagnostic accuracy study. Methods: A total of 600 adult patients were retrospectively included. The model combined static frame segmentation with dynamic tracking using a hybrid Simpson's method applied to apical 2-and 4-chamber views. Model performance was assessed against cardiologist measurements using Bland-Altman analysis. The YOLOv8 and ResNet50 models were employed for the wall motion module. Performance metrics, including accuracy, precision, F1 score, and area under the curve, were evaluated. Results: In the Bland-Altman analysis, the mean bias between the LVEF module and cardiologist measurements was-4, with limits of agreement ranging from-15 to-3. Regression analysis demonstrated a strong correlation between the LVEF module and cardiologist measurements (r = 0.71, p < 0.001). In the wall motion module, the YOLOv8 segmentation model exhibited high accuracy, while ResNet50 achieved superior performance with an accuracy of 95%. The algorithm's color coding contributed to standardized interpretation among operators, enhancing consistency. Conclusion: This is the first study to integrate automated EF calculation and WMA detection within a single workflow. SafeHeart offers accurate, reproducible, and rapid analysis, with the potential to support routine echocardiography practice. Color-coded region segmentation can facilitate more standardized and reliable results.
dc.description.sponsorshipRecep Tayyip Erdogan University through the Scientific Research Project (BAP) [TLO-2024-1711]; Turkish Technology Team Foundation (T3 Foundation); Teknofest Technology Festival organized underthe foundation
dc.description.sponsorshipThis project has been supported by Recep Tayyip Erdogan University through the Scientific Research Project (BAP) with the project code TLO-2024-1711. The project has also received funding support from the Turkish Technology Team Foundation (T3 Foundation) , and it achieved 4th place in Tuerkiye at the 2024 Teknofest Technology Festival organized underthe foundation.
dc.identifier.doi10.4274/balkanmedj.galenos.2025.2025-8-160
dc.identifier.endpage37
dc.identifier.issn2146-3123
dc.identifier.issn2146-3131
dc.identifier.issue1
dc.identifier.orcid0009-0000-0379-1002
dc.identifier.pmid41178565
dc.identifier.scopus2-s2.0-105026389560
dc.identifier.scopusqualityQ1
dc.identifier.startpage25
dc.identifier.trdizinid1372720
dc.identifier.urihttps://doi.org/10.4274/balkanmedj.galenos.2025.2025-8-160
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1372720
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5108
dc.identifier.volume43
dc.identifier.wosWOS:001657600000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherGalenos Publ House
dc.relation.ispartofBalkan Medical Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subject[Keyword Not Available]
dc.titleA Multimodular AI Algorithm for Automated Assessment of Left Ventricular Function in Ischemic Heart Disease: Ejection Fraction, Wall Motion, and Regional Myocardial Segmentation
dc.typeArticle

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