{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T19:23:15Z","timestamp":1792092195305,"version":"4.4.0"},"reference-count":38,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"governmental grants-aid-for-scientists","award":["23K08695"],"award-info":[{"award-number":["23K08695"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: The clinical management of adolescent idiopathic scoliosis (AIS) is hindered by the inability to accurately predict curve progression. Although skeletal maturity and the initial Cobb angle are established predictors of progression, their combined predictive accuracy remains limited. This study aimed to develop a robust and interpretable artificial intelligence (AI) system using deep learning (DL) models to predict the progression of scoliosis using only standing frontal radiographs. Methods: We conducted a multicenter study involving 542 patients with AIS. After excluding 52 borderline progression cases (6\u20139\u00b0 progression in the Cobb angle), 294 and 196 patients were assigned to progression (\u226510\u00b0 increase) and non-progression (\u22645\u00b0 increase) groups, respectively, considering a 2-year follow-up. Frontal whole spinal radiographs were preprocessed using histogram equalization and divided into two regions of interest (ROIs) (ROI 1, skull base\u2013femoral head; ROI 2, C7\u2013iliac crest). Six pretrained DL models, including convolutional neural networks (CNNs) and transformer-based models, were trained on the radiograph images. Gradient-weighted class activation mapping (Grad-CAM) was further performed for model interpretation. Results: Ensemble models outperformed individual ones, with the average ensemble model achieving area under the curve (AUC) values of 0.769 for ROI 1 and 0.755 for ROI 2. Grad-CAM revealed that the CNNs tended to focus on the local curve apex, whereas the transformer-based models demonstrated global attention across the spine, ribs, and pelvis. Models trained on ROI 2 performed comparably with respect to those using ROI 1, supporting the feasibility of image standardization without a loss of accuracy. Conclusions: This study establishes the clinical potential of transformer-based DL models for predicting the progression of scoliosis using only plain radiographs. Our multicenter approach, high AUC values, and interpretable architectures support the integration of AI into clinical decision-making for the early treatment of AIS.<\/jats:p>","DOI":"10.3390\/jcm14207216","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T11:57:42Z","timestamp":1760529462000},"page":"7216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Development and Validation of Transformer- and Convolutional Neural Network-Based Deep Learning Models to Predict Curve Progression in Adolescent Idiopathic Scoliosis"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6906-5406","authenticated-orcid":false,"given":"Shinji","family":"Takahashi","sequence":"first","affiliation":[{"name":"Department of Orthopaedic Surgery, Osaka Metropolitan University, 1-4-3 Asahimachi Abeno-ku, Osaka 545-8585, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4275-1422","authenticated-orcid":false,"given":"Shota","family":"Ichikawa","sequence":"additional","affiliation":[{"name":"Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-dori, Chuo-ku, Niigata 951-8518, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8743-7074","authenticated-orcid":false,"given":"Kei","family":"Watanabe","sequence":"additional","affiliation":[{"name":"Niigata Spine Surgery Center, Kameda Daiichi Hospital, Niigata 950-0165, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5195-9874","authenticated-orcid":false,"given":"Haruki","family":"Ueda","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Dokkyo Medical University, 880 Kitakobayashi, Shimotsuga, Mibu 321-0293, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0586-6818","authenticated-orcid":false,"given":"Hideyuki","family":"Arima","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Shinshu University School of Medicine, Hamamatsu 431-3192, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4701-8211","authenticated-orcid":false,"given":"Yu","family":"Yamato","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Shinshu University School of Medicine, Hamamatsu 431-3192, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takumi","family":"Takeuchi","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Kyorin University, Tokyo 181-8611, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2070-4043","authenticated-orcid":false,"given":"Naobumi","family":"Hosogane","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Kyorin University, Tokyo 181-8611, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masashi","family":"Okamoto","sequence":"additional","affiliation":[{"name":"Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-dori, Chuo-ku, Niigata 951-8518, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manami","family":"Umezu","sequence":"additional","affiliation":[{"name":"Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-dori, Chuo-ku, Niigata 951-8518, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroki","family":"Oba","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Shinshu University School of Medicine, Matsumoto 390-8621, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3744-0598","authenticated-orcid":false,"given":"Yohan","family":"Kondo","sequence":"additional","affiliation":[{"name":"Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-dori, Chuo-ku, Niigata 951-8518, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9930-5100","authenticated-orcid":false,"given":"Shoji","family":"Seki","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Faculty of Medicine, University of Toyama, 2630 Sugitani, Toyama 930-0194, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"519","DOI":"10.31616\/asj.2018.0096","article-title":"Etiology of Adolescent Idiopathic Scoliosis: A Literature Review","volume":"13","author":"Kikanloo","year":"2019","journal-title":"Asian Spine J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1007\/s00586-021-06817-0","article-title":"Scoliosis and Prognosis-a Systematic Review Regarding Patient-Specific and Radiological Predictive Factors for Curve Progression","volume":"30","author":"Lenz","year":"2021","journal-title":"Eur. 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