{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deeplasia-deep-learning-for-bone-age","title":"Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias","arxiv_id":null,"date":"2023-11-23","proceeding":"Pediatric Radiology 2023 11","authors":["Sebastian Rassmann","Alexandra Keller","Kyra Skaf","Alexander Hustinx","Ruth Gausche","Miguel A. Ibarra-Arrelano","Tzung-Chien Hsieh","Yolande E. D. Madajieu","Markus M. Nöthen","Roland Pfäffle","Ulrike I. Attenberger","Mark Born","Klaus Mohnike","Peter M. Krawitz & Behnam Javanmardi"],"abstract":"Skeletal dysplasias collectively affect a large number of patients worldwide. Most of these disorders cause growth anomalies. Hence, evaluating skeletal maturity via the determination of bone age (BA) is a useful tool. Moreover, consecutive BA measurements are crucial for monitoring the growth of patients with such disorders, especially for timing hormonal treatment or orthopedic interventions. However, manual BA assessment is time-consuming and suffers from high intra- and inter-rater variability. This is further exacerbated by genetic disorders causing severe skeletal malformations. While numerous approaches to automate BA assessment have been proposed, few are validated for BA assessment on children with skeletal dysplasias.","url_abs":"https://link.springer.com/article/10.1007/s00247-023-05789-1","url_pdf":"https://link.springer.com/content/pdf/10.1007/s00247-023-05789-1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deeplasia-deep-learning-for-bone-age","repo_url":"https://github.com/aimi-bonn/hand-segmentation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"deeplasia-deep-learning-for-bone-age","repo_url":"https://github.com/aimi-bonn/Deeplasia","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/on-bone-age","task":"","dataset":"Bone Age","model":"Deeplasia","rank_in_archive_order":1,"of":1,"metrics":{"MAD":"3.87"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}