ArticleFrontiers in artificial intelligence2024
The automated Greulich and Pyle: a coming-of-age for segmental methods?
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- digiBONE: an automated tool for segmental Greulich-Pyle bone age assessment of Indian children and adolescents.Frontiers in endocrinology · 2026Article
- Phalangeal bone growth and implications in Turner syndrome.Frontiers in endocrinology · 2025Article
- Bone Age in Endocrinology - Part 1: Methods of Bone Age Assessment.Indian journal of endocrinology and metabolismReview
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10 authors.
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Abstract
The well-known Greulich and Pyle (GP) method of bone age assessment (BAA) relies on comparing a hand X-ray against templates of discrete maturity classes collected in an atlas. Automated methods have recently shown great success with BAA, especially using deep learning. In this perspective, we first review the success and limitations of various automated BAA methods. We then offer a novel hypothesis: When networks predict bone age that is not aligned with a GP reference class, it is not simply statistical error (although there is that as well); they are picking up nuances in the hand X-ray that lie "outside that class." In other words, trained networks predict
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