AUTHOR=Chapke Rashmi , Mondkar Shruti , Oza Chirantap , Khadilkar Vaman , Aeppli Tim R. J. , Sävendahl Lars , Kajale Neha , Ladkat Dipali , Khadilkar Anuradha , Goel Pranay TITLE=The automated Greulich and Pyle: a coming-of-age for segmental methods? JOURNAL=Frontiers in Artificial Intelligence VOLUME=7 YEAR=2024 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1326488 DOI=10.3389/frai.2024.1326488 ISSN=2624-8212 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