Deep learning-based automatic measurement of the femoral head ossification center in healthy Korean children: development of a novel radiographic growth chart.

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Abstract

To develop and validate a deep learning (DL)-based algorithm for automated measurement of femoral head ossification center (FHOC) size and establish AI-derived growth charts. This retrospective study included 1705 healthy Korean children (mean age, 5.1 ± 3.3 years; 841 females, 864 males) with anteroposterior pelvic radiographs (2018-2024). A three-stage DL algorithm (region-of-interest detection, FHOC segmentation, landmark-based size computation) was used to automatically measure FHOC size. Agreement with radiologist measurements was evaluated using concordance correlation coefficient (CCC), Pearson correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman analyses, supplemented by paired t-test and Fisher's Z-test. AI measurements were used to create FHOC growth charts via quantile polynomial regression, with predictive accuracy assessed by adjusted R², MAE, and RMSE. AI-derived FHOC size measurements showed close agreement with radiologist measurements, with mean differences within ±0.5 mm and 95% limits of agreement within ±3 mm in age-stratified analyses, and overall agreement was further supported by high CCC, r, and consistently low error metrics. Growth curves based on AI measurements demonstrated strong predictive accuracy (adjusted R² = 0.927 for females; 0.934 for males), with low errors across age groups (females: MAE 1.77-2.98 mm, RMSE 2.28-3.54 mm; males: MAE 1.60-3.01 mm, RMSE 2.00-4.10 mm). Reference percentiles (5th-95th) were established, providing standardized FHOC size ranges for clinical application. Our DL-based approach provides precise and reproducible FHOC size measurement, offering a robust reference for standardized growth assessment and early pediatric hip joint evaluation. QuestionThe timing of FHOC appearance is an important radiographic indicator; however, manual measurement is subjective, and studies on age-specific changes remain limited. FindingsA DL-based algorithm achieved high agreement with expert measurements, and age-based regression reliably predicted FHOC size in children. Clinical relevanceAI-derived FHOC growth charts may provide objective, standardized references for pediatric hip joint development, potentially enabling earlier detection of growth abnormalities and improving diagnostic consistency in clinical practice.

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