Papers › Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias

Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias

23 Nov 2023Pediatric Radiology 2023 11archive 2025-07-28

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

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.

Code

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Results from the paper archive 2025-07-28

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections