{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T07:47:57Z","timestamp":1761551277390,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T00:00:00Z","timestamp":1761350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Objective: The purpose of this study is to present a kyphosis measurement method based on quadratic spline fitting through three key vertebral landmarks: T12, T8 and T4. This approach aims to capture thoracic spine curvature more continuously and accurately than traditional methods such as the Cobb angle and circle fitting. Methods: A dataset of 560 lateral thoracic spine radiographs was retrospectively analyzed, including cases of postural kyphosis, Scheuermann\u2019s disease, osteoporosis-induced kyphosis and ankylosing spondylitis. Two trained raters independently performed three repeated landmark annotations per image. The kyphosis angle was computed using two methods: (1) a quadratic spline fitted through the three landmarks, with the angle derived from tangent vectors at T12 and T4; and (2) a least-squares circle fit with the angle subtended between T12 and T4. Agreement with reference Cobb angles was evaluated using Pearson correlation, MAE, RMSE, ROC analysis and Bland\u2013Altman plots. Reliability was assessed using intraclass correlation coefficients (ICC). Results: Both methods showed excellent intra- and inter-rater reliability (ICC \u2265 0.967). The spline method achieved lower MAE (5.81\u00b0), lower RMSE (8.94\u00b0) and smaller bias compared to the circle method. Both methods showed strong correlation with Cobb angles (r \u2265 0.851) and excellent classification performance (AUC &gt; 0.950). Conclusions: Spline-based kyphosis measurement is accurate, reliable and particularly robust in cases with severe spinal deformity. Significance: This method supports automated, reproducible kyphosis assessment and may enhance clinical evaluation of spinal curvature using artificial intelligence-driven image analysis.<\/jats:p>","DOI":"10.3390\/diagnostics15212703","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T07:31:58Z","timestamp":1761550318000},"page":"2703","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quadratic Spline Fitting for Robust Measurement of Thoracic Kyphosis Using Key Vertebral Landmarks"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7668-2448","authenticated-orcid":false,"given":"Nikola","family":"Kirilov","sequence":"first","affiliation":[{"name":"Department of Orthopedics and Traumatology, Medical University-Pleven, 5800 Pleven, Bulgaria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena","family":"Bischoff","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Burgas State University \u201cProf. Dr. Assen Zlatarov\u201d, 8000 Burgas, Bulgaria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,25]]},"reference":[{"key":"ref_1","unstructured":"Lam, J.C., and Mukhdomi, T. 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