Bone Age Estimation and Prediction of Final Adult Height Using Deep Learning
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Purpose: The appropriate evaluation of height and accurate estimation of bone age are crucial for proper assessment of the growth status of a child. We developed a bone age estimation program using a deep learning algorithm and established a model to predict the final adult height of Korean children. Materials and Methods: A total of 1678 radiographs from 866 children, for which the interpretation results were consistent be tween two pediatric endocrinologists, were used to train and validate the deep learning model. The bone age estimation algorithm was based on the convolutional neural network of the deep learning system. The test set simulation was performed by a deep learning program and two raters using 150 radiographs and final height data for 100 adults. Results: There was a statistically significant correlation between bone age interpreted by the artificial intelligence (AI) program and the reference bone age in the test set simulation (r=0.99, p<0.001). In the test set simulation, the AI program showed a mean absolute error (MAE) of 0.59 years and a root mean squared error (RMSE) of 0.55 years, compared with reference bone age, and showed similar accuracy to that of an experienced pediatric endocrinologist (rater 1). Prediction of final adult height by the AI program showed an MAE of 4.62 cm, compared with the actual final adult height. Conclusion: We developed a bone age estimation program based on a deep learning algorithm. The AI-derived program demon strated high accuracy in estimating bone age and predicting the final adult height of Korean children and adolescents.
Abstract
Purpose: The appropriate evaluation of height and accurate estimation of bone age are crucial for proper assessment of the growth status of a child. We developed a bone age estimation program using a deep learning algorithm and established a model to predict the final adult height of Korean children. Materials and Methods: A total of 1678 radiographs from 866 children, for which the interpretation results were consistent be tween two pediatric endocrinologists, were used to train and validate the deep learning model. The bone age estimation algorithm was based on the convolutional neural network of the deep learning system. The test set simulation was performed by a deep learning program and two raters using 150 radiographs and final height data for 100 adults. Results: There was a statistically significant correlation between bone age interpreted by the artificial intelligence (AI) program and the reference bone age in the test set simulation (r=0.99, p<0.001). In the test set simulation, the AI program showed a mean absolute error (MAE) of 0.59 years and a root mean squared error (RMSE) of 0.55 years, compared with reference bone age, and showed similar accuracy to that of an experienced pediatric endocrinologist (rater 1). Prediction of final adult height by the AI program showed an MAE of 4.62 cm, compared with the actual final adult height. Conclusion: We developed a bone age estimation program based on a deep learning algorithm. The AI-derived program demon strated high accuracy in estimating bone age and predicting the final adult height of Korean children and adolescents.
참고문헌 (22)
- Allen DB, "Growth promotion ethics and the challenge to resist cosmetic endocrinology", Horm Res Paediatr, 87, pp.145~152 (2017)
- Greulich WW, "Radiographic atlas of skeletal development of the hand and wrist" (1959), Stanford University Press
- Tanner JM, "Assessment of skeletal maturity and prediction of adult height (TW2 method)" (1983), Academic Press
- Halabi SS, "The RSNA pediatric bone age machine learning challenge", Radiology, 290, pp.498~503 (2019)
- Lee H, "Fully automated deep learning system for bone age assessment", J Digit Imaging, 30, pp.427~441 (2017)
- Booz C, "Artificial intelligence in bone age assessment : accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method", Eur Radiol Exp, 4, pp.6~ (2020)
- Lee Byoung-Dai, "Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment", Korean Journal of Radiology, 22(5), pp.792~800 (2021), 대한영상의학회[DOI]
- Kim JR, "Computerized bone age estimation using deep learning based program : evaluation of the accuracy and efficiency", AJR Am J Roentgenol, 209, pp.1374~1380 (2017)
- Lea WW, "External validation of deep learning-based bone-age software : a preliminary study with real world data", Sci Rep, 12, pp.1232~ (2022)
- "YOLOv5"[URL]
- Shastry AS, "Automatic bone age assessment of radiographs using deep learning", Proceedings of the 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), pp.1~7 (2021), IEEE
- "Very deep convolutional networks for large-scale image recognition" (2014)[URL]
- Woo S, "Cbam : convolutional block attention module", Proceedings of the European Conference on Computer Vision (ECCV), pp.3~19 (2018), ECCV
- "BAM: bottleneck attention module" (2018)[URL]
- 김재현, "The 2017 Korean National Growth Charts for children and adolescents: development, improvement, and prospects", Clinical and Experimental Pediatrics, 61(5), pp.135~149 (2018), 대한소아청소년과학회
- Cole TJ, "The LMS method for constructing normalized growth standards", Eur J Clin Nutr, 44, pp.45~60 (1990)
- Landis JR, "The measurement of observer agreement for categorical data", Biometrics, 33, pp.159~174 (1977)
- McBride GB, "Using statistical methods for water quality management:issues, problems and solutions" (2005), John Wiley &Sons
- Martin DD, "Validation of a new method for automated determination of bone age in Japanese children", Horm Res Paediatr, 73, pp.398~404 (2010)
- Bui TD, "Incorporated region detection and classification using deep convolutional networks for bone age assessment", Artif Intell Med, 97, pp.1~8 (2019)
- Son SJ, "TW3-based fully automated bone age assessment system using deep neural networks", IEEE Access, 7, pp.33346~33358 (2019)
- Sperling MA, "Pediatric endocrinology" (2014), Saunders