{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T22:38:04Z","timestamp":1783463884926,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T00:00:00Z","timestamp":1744588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Health and Welfare","award":["HI20C2092"],"award-info":[{"award-number":["HI20C2092"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: To develop an automated deep learning-based bone age prediction model using the Tanner\u2013Whitehouse (TW3) method and evaluate its feasibility by comparing its performance with that of pediatric radiologists. Methods: The hand and wrist radiographs of 560 Korean children and adolescents (280 female, 280 male, mean age 9.43 \u00b1 2.92 years) were evaluated using the TW3-based model and three pediatric radiologists. Images with bony destruction, congenital anomalies, or non-diagnostic quality were excluded. A commercialized AI solution built upon the Rotated Single Shot MultiBox Detector (SSD) and EfficientNet-B0 was used. Bone age measurements from the model and radiologists were compared using the paired t-tests. Linear regression analysis was performed and the coefficient of determination (r\u00b2), mean absolute error (MAE), and root mean square error (RMSE) were measured. A Bland\u2013Altman analysis was conducted and the proportion of bone age predictions within 0.6 years of the radiologists\u2019 assessments was calculated. Results: The TW3-based model demonstrated no significant differences between bone age measurements and radiologists, except for participants &lt;6 and &gt;13 years old (overall, p = 0.874; 6\u20138 years, p = 0.737; 8\u20139 years, p = 0.093; 9\u201310 years, p = 0.301; 10\u201311 years, p = 0.584; 11\u201313 years, p = 0.976; &lt;6 or &gt;13 years, p &lt; 0.001). There was a strong linear correlation between the model prediction and radiologist assessments (r2 = 0.977). The RMSE and MAE values of the model were 0.529 (95% CI, 0.482\u20130.575) and 0.388 (95% CI, 0.361\u20130.417) years. Overall, 82.3% of bone age model predictions were within 0.6 years of the radiologists\u2019 interpretation. Conclusions: Automated deep learning-based bone age assessment has the potential to reduce radiologists\u2019 workload and provide standardized measurements for clinical decision making.<\/jats:p>","DOI":"10.3390\/diagnostics15080993","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T08:11:39Z","timestamp":1744704699000},"page":"993","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Retrospective Clinical Trial to Evaluate the Effectiveness of a New Tanner\u2013Whitehouse-Based Bone Age Assessment Algorithm Trained with a Deep Neural Network System"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8143-0018","authenticated-orcid":false,"given":"Meesun","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young-Hun","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea"},{"name":"Department of Radiology, Seoul National University College of Medicine, Jongno-gu, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seul-Bi","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea"},{"name":"Department of Radiology, Seoul National University College of Medicine, Jongno-gu, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5937-7238","authenticated-orcid":false,"given":"Jae-Won","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea"},{"name":"Department of Radiology, Seoul National University College of Medicine, Jongno-gu, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1858-0640","authenticated-orcid":false,"given":"Seunghyun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea"},{"name":"Department of Radiology, Seoul National University College of Medicine, Jongno-gu, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae-Yeon","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea"},{"name":"Department of Radiology, Seoul National University College of Medicine, Jongno-gu, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung-Eun","family":"Cheon","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea"},{"name":"Department of Radiology, Seoul National University College of Medicine, Jongno-gu, Seoul 03080, Republic of Korea"},{"name":"Institute of Radiation Medicine, Seoul National University Medical Research Center, Jongno-gu, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"SungHyuk","family":"Hong","sequence":"additional","affiliation":[{"name":"Healthhub Co., Ltd., AI Lab., Seoul 04175, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeonghoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Healthhub Co., Ltd., AI Lab., Seoul 04175, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9820-3030","authenticated-orcid":false,"given":"Yeon-Jin","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea"},{"name":"Department of Radiology, Seoul National University College of Medicine, Jongno-gu, Seoul 03080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cavallo, F., Mohn, A., Chiarelli, F., and Giannini, C. 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