Proof-of-concept comparison of an artificial intelligence-based bone age assessment tool with Greulich-Pyle and Tanner-Whitehouse version 2 methods in a pediatric cohort

Luca Marinelli; Antonio Lo Mastro; Francesca Grassi; Daniela Berritto; Anna Russo; Vittorio Patanè; Anna Festa; Enrico Grassi; Anna Grandone; Luigi Aurelio Nasto; Enrico Pola; Alfonso Reginelli

doi:10.1007/s00247-025-06405-0

Proof-of-concept comparison of an artificial intelligence-based bone age assessment tool with Greulich-Pyle and Tanner-Whitehouse version 2 methods in a pediatric cohort

Pediatr Radiol. 2025 Sep 25. doi: 10.1007/s00247-025-06405-0. Online ahead of print.

Affiliations

¹ Department of Precision Medicine, University of Campania, Luigi Vanvitelli, Piazza Miraglia 2, 80138, Naples, Italy. mariner189@gmail.com.
² Department of Precision Medicine, University of Campania, Luigi Vanvitelli, Piazza Miraglia 2, 80138, Naples, Italy.
³ University of Foggia, Foggia, Italy.

PMID: 40996527
DOI: 10.1007/s00247-025-06405-0

Abstract

Background: Bone age assessment is essential in evaluating pediatric growth disorders. Artificial intelligence (AI) systems offer potential improvements in accuracy and reproducibility compared to traditional methods.

Objective: To compare the performance of a commercially available artificial intelligence-based software (BoneView BoneAge, Gleamer, Paris, France) against two human-assessed methods-the Greulich-Pyle (GP) atlas and Tanner-Whitehouse version 2 (TW2)-in a pediatric population.

Materials and methods: This proof-of-concept study included 203 pediatric patients (mean age, 9.0 years; range, 2.0-17.0 years) who underwent hand and wrist radiographs for suspected endocrine or growth-related conditions. After excluding technically inadequate images, 157 cases were analyzed using AI and GP-assessed methods. A subset of 35 patients was also evaluated using the TW2 method by a pediatric endocrinologist. Performance was measured using mean absolute error (MAE), root mean square error (RMSE), bias, and Pearson's correlation coefficient, using chronological age as reference.

Results: The AI model achieved a MAE of 1.38 years, comparable to the radiologist's GP-based estimate (MAE, 1.30 years), and superior to TW2 (MAE, 2.86 years). RMSE values were 1.75 years, 1.80 years, and 3.88 years, respectively. AI showed minimal bias (-0.05 years), while TW2-based assessments systematically underestimated bone age (bias, -2.63 years). Strong correlations with chronological age were observed for AI (r=0.857) and GP (r=0.894), but not for TW2 (r=0.490).

Conclusion: BoneView demonstrated comparable accuracy to radiologist-assessed GP method and outperformed TW2 assessments in this cohort. AI-based systems may enhance consistency in pediatric bone age estimation but require careful validation, especially in ethnically diverse populations.

Keywords: Artificial intelligence; Bone development; Child; Growth disorders; Hand bones; Pediatrics; Radiography.