Combined assisted bone age assessment and adult height prediction methods in Chinese girls with early puberty: analysis of three artificial intelligence systems

Abstract

Background The applicability and accuracy of artificial intelligence (AI)-assisted bone age assessment and adult height prediction methods in girls with early puberty are unknown. Objective To analyze the performance of AI-assisted bone age assessment methods by comparing the corresponding methods for predicted adult height with actual adult height. Materials and methods 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. Results 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. Conclusion 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.

STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning

“…U-net [ 22 ] is based on FCN [ 23 ] and exhibits the advantages of good performance, low data requirement, and high speed. Various segmentation methods based on deep convolutional neural networks (CNNs), including crowd counting [ 24 , 25 ], text recognition [ 26 , 27 ], and medical image analysis [ 3 , 28 , 29 , 30 , 31 ], have been proposed and are widely used to obtain statistics on target objects [ 32 , 33 , 34 ].…”