Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohort

Abstract

Purpose The determination of bone age (BA) based on the hand and wrist, using the 70-year-old Greulich and Pyle (G&P) atlas, remains a widely employed practice in various institutions today. However, a more recent approach utilizing artificial intelligence (AI) enables automated BA estimation based on the G&P atlas. Nevertheless, AI-based methods encounter limitations when dealing with images that deviate from the standard hand and wrist projections. Generally, the extent to which BA, as determined by the G&P atlas, corresponds to the chronological age (CA) of a contemporary German population remains a subject of continued discourse. This study aims to address two main objectives. Firstly, it seeks to investigate whether the G&P atlas, as applied by the AI software, is still relevant for healthy children in Germany today. Secondly, the study aims to assess the performance of the AI software in handling non-strict posterior-anterior (p. a.) projections of the hand and wrist.

Materials and Methods The AI software retrospectively estimated the BA in children who had undergone radiographs of a single hand using posterior-anterior and oblique planes. The primary purpose was to rule out any osseous injuries. The prediction error of BA in relation to CA was calculated for each plane and between the two planes.

Results A total of 1253 patients (aged 3 to 16 years, median age 10.8 years, 55.7 % male) were included in the study. The average error of BA in posterior-anterior projections compared to CA was 3.0 (± 13.7) months for boys and 1.7 (± 13.7) months for girls. Interestingly, the deviation from CA tended to be even slightly lower in oblique projections than in posterior-anterior projections. The mean error in the posterior-anterior projection plane was 2.5 (± 13.7) months, while in the oblique plane it was 1.8 (± 13.9) months (p = 0.01).

Conclusion The AI software for BA generally corresponds to the age of the contemporary German population under study, although there is a noticeable prediction error, particularly in younger children. Notably, the software demonstrates robust performance in oblique projections.

Key Points 

1. Bone age, as determined by artificial intelligence, aligns with the chronological age of the contemporary German cohort under study.

2. As determined by artificial intelligence, bone age is remarkably robust, even when utilizing oblique X-ray projections.

Citation Format

• Pape J, Hirsch F, Deffaa O et al. Applicability and robustness of an artificial intelligence-based assessment for Greulich and Pyle bone age in a German cohort. Fortschr Röntgenstr 2024; 196: 600 – 606

Zusammenfassung

Ziel Die Bestimmung des Knochenalters (BA) anhand der Hand und des Handgelenks unter Verwendung des 70 Jahre alten Atlas von Greulich und Pyle (G&P) ist auch heute noch eine weit verbreitete Praxis in verschiedenen Einrichtungen. Ein neuerer Ansatz, basierend auf dem Einsatz künstlicher Intelligenz (KI), ermöglicht eine automatische BA-Schätzung auf der Grundlage des G&P-Atlas. Allerdings stoßen KI-basierte Methoden an ihre Grenzen, wenn es um Bilder geht, die von den Standardprojektionen der Hand und des Handgelenks abweichen. Nach wie vor ist umstritten, inwieweit das mit dem G&P-Atlas ermittelte BA dem chronologischen Alter (CA) der heutigen deutschen Bevölkerung entspricht. Mit dieser Studie werden zwei Hauptziele verfolgt. Erstens soll untersucht werden, ob der G&P-Atlas, wie er von der KI-Software angewendet wird, für gesunde Kinder in Deutschland heute noch relevant ist. Zweitens zielt die Studie darauf ab, die Leistung der KI-Software bei der Handhabung nicht-strikter posterior-anteriorer (p. a.) Projektionen der Hand und des Handgelenks zu bewerten.

Materialien und Methoden Die AI-Software schätzte retrospektiv die BA bei Kindern, die sich Röntgenaufnahmen einer einzelnen Hand unter Verwendung von posterior-anterioren und schrägen Ebenen unterzogen hatten. Der Hauptzweck bestand darin, knöcherne Verletzungen auszuschließen. Der Vorhersagefehler des BA im Verhältnis zum CA wurde für jede Ebene und zwischen den beiden Ebenen berechnet.

Ergebnisse Insgesamt wurden 1253 Patienten (im Alter von 3 bis 16 Jahren, medianes Alter 10,8 Jahre, 55,7 % männlich) in die Studie aufgenommen. Die durchschnittliche Abweichung des BA in posterior-anterioren Projektionen im Vergleich zum CA betrug bei Jungen 3,0 (± 13,7) Monate und 1,7 (± 13,7) Monate bei Mädchen. Interessanterweise war die Abweichung des BA vom CA in den schrägen Projektionen tendenziell etwas geringer als in den posterior-anterioren Projektionen. Der mittlere Fehler in der posterior-anterioren Projektionsebene betrug 2,5 (± 13,7) Monate, während er in der schrägen Ebene bei 1,8 (± 13,9) Monaten lag (p = 0,01).

Schlussfolgerung Das mittels KI-Software ermittelte BA entspricht im Allgemeinen dem Alter der deutschen Untersuchungspopulation, obwohl es einen merklichen Vorhersagefehler gibt, insbesondere bei jüngeren Kindern. Insbesondere bei schrägen Projektionen zeigt die Software eine robuste Leistung.

Kernaussagen 

1. Das von der künstlichen Intelligenz ermittelte Knochenalter stimmt mit dem chronologischen Alter der untersuchten deutschen Alterskohorte überein.

2. Das durch künstliche Intelligenz ermittelte Knochenalter ist bemerkenswert stabil, auch bei der Verwendung schräger Röntgenprojektionen.

Keywords

children - radiography - artificial intelligence - bone age - Greulich&Pyle

Publication History

Received: 09 June 2023

Accepted: 04 October 2023

Article published online:
08 December 2023

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