{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T14:44:36Z","timestamp":1768315476694,"version":"3.49.0"},"reference-count":27,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Left ventricular non-compaction is a cardiac condition marked by excessive trabeculae in the left ventricle\u2019s inner wall. Although various methods exist to measure these structures, the medical community still lacks consensus on the best approach. Previously, we developed DL-LVTQ, a tool based on a UNet neural network, to quantify trabeculae in this region. In this study, we expand the dataset to include new patients with Titin cardiomyopathy and healthy individuals with fewer trabeculae, requiring retraining of our models to enhance predictions. We also propose ViTUNeT, a neural network architecture combining U-Net and Vision Transformers to segment the left ventricle more accurately. Additionally, we train a YOLOv8 model to detect the ventricle and integrate it with ViTUNeT model to focus on the region of interest. Results from ViTUNet and YOLOv8 are similar to DL-LVTQ, suggesting dataset quality limits further accuracy improvements. To test this, we analyze MRI images and develop a method using two YOLOv8 models to identify and remove problematic images, leading to better results. Combining YOLOv8 with deep learning networks offers a promising approach for improving cardiac image analysis and segmentation.<\/jats:p>","DOI":"10.1515\/jib-2024-0048","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T03:11:29Z","timestamp":1748920289000},"source":"Crossref","is-referenced-by-count":1,"title":["A ViTUNeT-based model using YOLOv8 for efficient LVNC diagnosis and automatic cleaning of dataset"],"prefix":"10.1515","volume":"22","author":[{"given":"Salvador","family":"de Haro","sequence":"first","affiliation":[{"name":"Computer Engineering Department , 16751 University of Murcia , 30100 Murcia , Spain"}]},{"given":"Gregorio","family":"Bernab\u00e9","sequence":"additional","affiliation":[{"name":"Computer Engineering Department , 16751 University of Murcia , 30100 Murcia , Spain"}]},{"given":"Jos\u00e9 Manuel","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"Computer Engineering Department , 16751 University of Murcia , 30100 Murcia , Spain"}]},{"given":"Pilar","family":"Gonz\u00e1lez-F\u00e9rez","sequence":"additional","affiliation":[{"name":"Computer Engineering Department , 16751 University of Murcia , 30100 Murcia , Spain"}]}],"member":"374","published-online":{"date-parts":[[2025,6,4]]},"reference":[{"key":"2025102907580416394_j_jib-2024-0048_ref_001","doi-asserted-by":"crossref","unstructured":"Mc Namara, K, Alzubaidi, H, Jackson, JK. Cardiovascular disease as a leading cause of death: how are pharmacists getting involved? Integrated Pharm Res Pract 2019;8:1\u201311. https:\/\/doi.org\/10.2147\/iprp.s133088.","DOI":"10.2147\/IPRP.S133088"},{"key":"2025102907580416394_j_jib-2024-0048_ref_002","unstructured":"World Health Organization. Cardiovascular diseases; 2022. Available from: https:\/\/www.who.int\/health-topics\/cardiovascular-diseases."},{"key":"2025102907580416394_j_jib-2024-0048_ref_003","doi-asserted-by":"crossref","unstructured":"Towbin, JA, Lorts, A, Jefferies, JL. Left ventricular non-compaction cardiomyopathy. Lancet 2015;386:813\u201325. https:\/\/doi.org\/10.1016\/s0140-6736(14)61282-4.","DOI":"10.1016\/S0140-6736(14)61282-4"},{"key":"2025102907580416394_j_jib-2024-0048_ref_004","doi-asserted-by":"crossref","unstructured":"Arbustini, E, Weidemann, F, Hall, JL. Left ventricular noncompaction: a distinct cardiomyopathy or a trait shared by different cardiac diseases? J Am Coll Cardiol 2014;64:1840\u201350. https:\/\/doi.org\/10.1016\/j.jacc.2014.08.030.","DOI":"10.1016\/j.jacc.2014.08.030"},{"key":"2025102907580416394_j_jib-2024-0048_ref_005","doi-asserted-by":"crossref","unstructured":"Biagini, E, Ragni, L, Ferlito, M, Pasquale, F, Lofiego, C, Leone, O, et al.. Different types of cardiomyopathy associated with isolated ventricular noncompaction. Am J Cardiol 2006;98:821\u20134. https:\/\/doi.org\/10.1016\/j.amjcard.2006.04.021.","DOI":"10.1016\/j.amjcard.2006.04.021"},{"key":"2025102907580416394_j_jib-2024-0048_ref_006","doi-asserted-by":"crossref","unstructured":"Udeoji, DU, Philip, KJ, Morrissey, RP, Phan, A, Schwarz, ER. Left ventricular noncompaction cardiomyopathy: updated review. 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