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• W4313236723 abstract "The applicability and accuracy of artificial intelligence (AI)-assisted bone age assessment and adult height prediction methods in girls with early puberty are unknown.To analyze the performance of AI-assisted bone age assessment methods by comparing the corresponding methods for predicted adult height with actual adult height.This retrospective review included 726 girls with early puberty, 87 of whom had reached adult height at last follow-up. Bone age was evaluated using the Greulich-Pyle (GP), Tanner-Whitehouse (TW3-RUS) and China 05 RUS-CHN (RUS-CHN) methods. Predicted adult height was calculated using the China 05 (CH05), TW3 and Bayley-Pinneau (BP) methods.We analyzed 1,663 left-hand radiographs, including 155 from girls who had reached adult height. In the 6-8- and 9-11-years age groups, bone age differences were smaller than those in the 12-14-years group; however, the differences between predicted adult height and actual adult height were larger than those in the 12-14-years group. TW3 overestimated adult height by 0.4±2.8 cm, while CH05 and BP significantly underestimated adult height by 2.9±3.6 cm and 1.3±3.8 cm, respectively. TW3 yielded the highest proportion of predicted adult height within ±5 cm of actual adult height (92.9%), with the highest correlation between predicted and actual adult heights.The differences in measured bone ages increased with increasing bone age. However, the corresponding method for predicting adult height was more accurate when the bone age was older. TW3 might be more suitable than CH05 and BP for predicting adult height in girls with early puberty. Methods for predicting adult height should be optimized for populations of the same ethnicity and disease." @default.
• W4313236723 created "2023-01-06" @default.
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• W4313236723 date "2022-12-28" @default.
• W4313236723 modified "2023-10-01" @default.
• W4313236723 title "Combined assisted bone age assessment and adult height prediction methods in Chinese girls with early puberty: analysis of three artificial intelligence systems" @default.
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• W4313236723 doi "https://doi.org/10.1007/s00247-022-05569-3" @default.
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