Clinical application differences between TW3-Carpal and TW3-RUS based artificial intelligence-assisted bone age assessment system: a real-world pilot study
10.3760/cma.j.cn112149-20220330-00288
- VernacularTitle:真实世界中基于人工智能骨龄测量系统TW3-Carpal和TW3-RUS的临床应用差异
- Author:
Yang YANG
1
;
Chi WANG
;
Ling OU
;
Fengsen BAI
;
Xinyu YUAN
Author Information
1. 首都儿科研究所附属儿童医院放射科，北京 100020
- Keywords:
Child;
Age determination by skeleton;
Artificial intelligence;
Tanner-Whitehouse method
- From:
Chinese Journal of Radiology
2023;57(4):359-363
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the differences between Tanner-Whitehouse (TW)3-Carpal and TW3-RUS(radius, ulna and short bone)-based artificial intelligence (AI)-assisted bone age assessment system using real world data.Methods:The image data of 262 children who received X-ray examination of left wrist in the Affiliated Children′s Hospital, Capital Institute of Pediatrics from July to September 2021 were retrospectively collected. The AI bone age assistant methods based on TW3-RUS and TW3-Carpal criteria were used to obtain the bone age results, respectively. Two senior pediatric radiologists evaluated the bone age on the basis of TW3-RUS and TW3-Carpal criteria, and the averaged values of two reviewers was calculated and taken as the gold standard reference. The cases were stratified into six age groups at 3-year intervals according to the gold standard reference, including 1-3 ( n=10), 4-6 ( n=35), 7-9 ( n=70), 10-12 ( n=118), 13-15 ( n=27) and 16-18 ( n=2) years old groups. Intraclass correlation coefficient (ICC) was used to evaluate the consistency between AI results and the gold standard bone age results. Pearson correlation method was used to measure the reliability between AI results and the gold standard results. The difference of bone age results between using TW3-RUS and TW3-Carpal criteria in different age groups was compared using paired t-test. Results:As for the whole sample, the results based on TW3-RUS criteria were 8.9±3.1 years old for AI assessment and 8.7±2.9 years old for the golden standard reference, with the ICC of 0.983; and the results based on TW3-Carpal criteria were 8.7±3.0 years old for AI and 8.8±2.8 years old for the golden standard reference, with the ICC of 0.976. Positive correlation were found in both TW3-RUS ( r=0.985, P<0.001) and TW3-Carpal criteria groups ( r=0.978, P<0.001). There were significant differences between TW3-RUS and TW3-Carpal at age groups of 7-9( t=-3.36, P=0.001), 10-12( t=-1.77, P=0.046), and 13-15 years old ( t=1.84, P=0.040). The bone age assessment using TW3-RUS and TW3-Carpal criteria were both in good agreement with the gold standard reference in age group of 4-6 years old (ICC=0.929 and 0.940), as well as in age group of 7-9 years old (ICC=0.882 and 0.927, respectively), with the results using TW3-Carpal criteria were slightly higher. As for the age groups of 10-12 and 13-15 years old, the method using TW3-RUS criteria showed excellent agreement with the gold standard reference (ICC=0.962 and 0.963, respectively), which were better than the performance of method using TW3-Carpal criteria (ICC=0.744 and 0.605, respectively). Conclusions:AI-assisted bone age system based TW3-Carpal and TW3-RUS criteria both show good reliability and accuracy in the bone age measurements. The AI method based TW3-Carpal criteria shows better performance in age group of 4-9 years old, while the method based on TW3-RUS criteria may be better for children of age 10-15 years old.
