{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:15:15Z","timestamp":1785413715845,"version":"3.56.0"},"reference-count":53,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004410","name":"Scientific and Technological Research Council of Turkey","doi-asserted-by":"publisher","award":["22AG040"],"award-info":[{"award-number":["22AG040"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"award":["22AG040"],"award-info":[{"award-number":["22AG040"]}],"id":[{"id":"https:\/\/ror.org\/04w9kkr77","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Rotator cuff tears are a leading cause of shoulder disability. While multi-sequence MRI is standard, the optimal deep learning integration of heterogeneous image series and clinical metadata remains unresolved. This study evaluated a hierarchical, sequence-aware multimodal framework for patient-level binary rotator cuff tear classification. Methods: A single-center cohort of 199 patients (100 tears, 99 controls) was analyzed across four MRI sequences (T1 coronal, T2 fat-suppressed sagittal, and proton density [PD] fat-suppressed coronal and transverse\/axial) and nine demographic features. Under a patient-level stratified three-fold cross-validation scheme preventing data leakage, we evaluated ResNet50 and Vision Transformer baselines (Study 0), full-protocol fusion topologies (Study 1), and systematically mapped sequence-subset combinations with or without metadata (Study 2). Results: In Study 0, the PD coronal ResNet50 model was the top baseline (AUC = 0.9834, F1 = 0.9515). In Study 1, late decision fusion yielded the highest AUC (0.9909), while feature concatenation optimized threshold balance (F1 = 0.9502). In Study 2, a streamlined three-sequence subset with metadata (C14M: T2 + PDc + PDt) achieved peak performance (AUC = 0.9961, 95% CI: 0.9823\u20130.9987, F1 = 0.9618, MCC = 0.9238), outperforming the full protocol (AUC = 0.9909, F1 = 0.9355). Metadata utility was configuration-dependent, assisting only fluid-sensitive combinations. Conclusions: Rather than indiscriminately aggregating entire clinical protocols, multimodal fusion is optimized by selecting complementary imaging series. For binary classification, excluding non-fat-suppressed T1 images in favor of a streamlined T2 and PD set stabilized by clinical demographics maximized classification performance in this internally validated, single-center cohort.<\/jats:p>","DOI":"10.3390\/jcm15145525","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T01:05:58Z","timestamp":1784077558000},"page":"5525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Multimodal Fusion of Multi-Sequence MRI and Clinical Metadata for the Classification of Rotator Cuff Tears"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3388-4047","authenticated-orcid":false,"given":"Sergen","family":"A\u015f\u0131k","sequence":"first","affiliation":[{"name":"Department of Software Engineering, Faculty of Engineering and Architecture, Eski\u015fehir Osmangazi University, Eski\u015fehir 26040, T\u00fcrkiye"},{"name":"Center of Intelligent Systems Applications Research, Eski\u015fehir Osmangazi University, Eski\u015fehir 26040, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5589-2032","authenticated-orcid":false,"given":"Ahmet","family":"Yaz\u0131c\u0131","sequence":"additional","affiliation":[{"name":"Center of Intelligent Systems Applications Research, Eski\u015fehir Osmangazi University, Eski\u015fehir 26040, T\u00fcrkiye"},{"name":"Department of Computer Engineering, Faculty of Engineering and Architecture, Eski\u015fehir Osmangazi University, Eski\u015fehir 26040, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8495-3161","authenticated-orcid":false,"given":"Murat","family":"A\u015f\u00e7\u0131","sequence":"additional","affiliation":[{"name":"Department of Orthopedics and Traumatology, Faculty of Medicine, Bilecik \u015eeyh Edebali University, Bilecik 11230, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u0130rfan","family":"Okumu\u015fer","sequence":"additional","affiliation":[{"name":"Department of Radiology, Yunus Emre State Hospital, Ministry of Health, Eski\u015fehir 26190, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1038\/s41572-024-00492-3","article-title":"Rotator Cuff Tears","volume":"10","author":"Bedi","year":"2024","journal-title":"Nat. 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