Automated Bone Age Assessment and Adult Height Prediction from Pediatric Hand Radiographs via a Cascaded Deep Learning Framework

Abstract

Bone age assessment and adult height prediction are essential for evaluating pediatric growth. Traditional methods rely on manual radiographic interpretation, which is subjective, time-consuming, and prone to inter-observer variability. This study presents an automated approach using a cascaded deep learning model to assess bone age and predict adult height from pediatric hand radiographs, aiming to improve diagnostic objectivity and efficiency. A total of 8,242 left-hand radiographs from Chinese children were retrospectively collected. Bone age was annotated by experienced pediatric endocrinologists using the China-05 standard. The model employed Yolact for instance segmentation to detect and classify bone structures, followed by parallel ResNet-18 subnetworks to grade ossification centers in the radius, ulna, and metacarpal/phalangeal bones. Predicted grades were integrated using a standardized scoring system to estimate bone age. A regression model then predicted adult height based on these features. The model achieved a Pearson correlation of 0.98 ([Formula: see text]) for bone age and 0.94 ([Formula: see text]) for adult height predictions. Bland-Altman analysis showed minimal bias and narrow limits of agreement. Mean absolute errors were 0.25 years for bone age and 1.75 cm for adult height. Average inference time was 7.8 seconds, significantly enhancing clinical efficiency. The proposed cascaded deep learning model delivers accurate, efficient, and reliable bone age assessment and adult height prediction, offering strong potential for clinical integration in pediatric growth evaluation.

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