A comparative study of the interpretation results of different artificial intelligence bone age assessment software

To compare the interpretation results of different AI bone age assessment software using the TW3 (Tanner-Whitehouse 3) method.

The comprehensive analysis included bone age readings for various age groups (3-17 years) using five methods: reference-TW3, YIZHUN-TW3, LIANYING-TW3, AIBAA-TW3, and manual-TW3. Researchers calculated mean values and standard deviations, employed linear regression analysis for predictive accuracy comparison, and generated Bland-Altman plots to assess agreement with the reference standard.

The comparative analysis of different TW3 methods revealed distinct variations in predictive accuracy. Notably, the YIZHUN-TW3 and LIANYING-TW3 models exhibited exceptional predictive accuracy, with multiple R-squared values indicating strong correlation with the reference standard (0.9861 for both methods). This was markedly higher than the AIBAA-TW3 model, which had a multiple R-squared value of 0.9591, and the manual-TW3 method with 0.9732. Statistical significance testing further confirmed the superiority of YIZHUN-TW3 and LIANYING-TW3, with p-values < 0.001 in linear regression analysis, while AIBAA-TW3 and manual-TW3 showed non-significant results in some comparisons. The AIBAA-TW3 model consistently predicted lower bone ages across various age groups, particularly in teenagers, with a notable underestimation trend. This pattern was supported by Bland-Altman analysis, which showed a mean difference of -0.6928 years for AIBAA-TW3, indicating a substantial underestimation.

The study highlights the advanced predictive accuracy and reliability of the YIZHUN-TW3 and LIANYING-TW3 methods in bone age assessment. While the AIBAA-TW3 method showed strong predictive power, its consistent underestimation trend suggests a need for recalibration.