Peer Review #1 of "Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development (v0.2)"

Objective Bone age (BA) is a crucial indicator for revealing the growth and development of children. This study tested the performance of a fully automated artificial intelligence (AI) system for BA assessment of Chinese children with abnormal growth and development. Materials and Methods A fully automated AI system based on the Greulich and Pyle (GP) method was developed for Chinese children by using 8,000 BA radiographs from five medical centers nationwide in China. Then, a total of 745 cases (360 boys and 385 girls) with abnormal growth and development from another tertiary medical center of north China were consecutively collected between January and October 2018 to test the system. The reference standard was defined as the result interpreted by two experienced reviewers (a radiologist with 10 years and an endocrinologist with 15 years of experience in BA reading) through consensus using the GP atlas. BA accuracy within 1 year, root mean square error (RMSE), mean absolute difference (MAD), and 95% limits of agreement according to the Bland-Altman plot were statistically calculated. Results For Chinese pediatric patients with abnormal growth and development, the accuracy of this new automated AI system within 1 year was 84.60% as compared to the reference standard, with the highest percentage of 89.45% in the 12- to 18-year group. The RMSE, MAD, and 95% limits of agreement of the AI system were 0.76 years, 0.58 years, and −1.547 to 1.428, respectively, according to the Bland-Altman plot. The largest difference between the AI and experts’ BA result was noted for patients of short stature with bone deformities, severe osteomalacia, or different rates of maturation of the carpals and phalanges. Conclusions The developed automated AI system could achieve comparable BA results to experienced reviewers for Chinese children with abnormal growth and development.

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Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

Pediatric Radiology ◽

10.1007/s00247-021-05130-8 ◽

2021 ◽

Author(s):

Amaka C. Offiah

Keyword(s):

Artificial Intelligence ◽

Pediatric Radiology ◽

Bone Age ◽

Future Research ◽

Fracture Detection ◽

Musculoskeletal Radiology ◽

Age Assessment ◽

Adult World ◽

Bone Age Assessment

AbstractArtificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research.

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Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method

European Radiology Experimental ◽

10.1186/s41747-019-0139-9 ◽

2020 ◽

Vol 4 (1) ◽

Cited By ~ 4

Author(s):

Christian Booz ◽

Ibrahim Yel ◽

Julian L. Wichmann ◽

Sabine Boettger ◽

Ahmed Al Kamali ◽

...

Keyword(s):

Artificial Intelligence ◽

Bone Age ◽

Age Assessment ◽

Automated Algorithm ◽

Bone Age Assessment

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Performance of an artificial intelligence system for bone age assessment in Tibet

British Journal of Radiology ◽

10.1259/bjr.20201119 ◽

2021 ◽

Vol 94 (1120) ◽

pp. 20201119

Author(s):

Fengdan Wang ◽

Wangjiu Cidan ◽

Xiao Gu ◽

Shi Chen ◽

Wu Yin ◽

...

Keyword(s):

Artificial Intelligence ◽

Southern China ◽

Medical Center ◽

Bone Age ◽

Absolute Difference ◽

Bone Maturation ◽

Mean Absolute Difference ◽

Left Hand ◽

Intelligence System ◽

Bone Age Assessment

Objective: To investigate whether bone age (BA) of children living in Tibet Highland could be accurately assessed using a fully automated artificial intelligence (AI) system. Methods： Left hand radiographs of 385 children (300 Tibetan and 85 immigrant Han) aged 4–18 years who presented to the largest medical center of Tibet between September 2013 and November 2019 were consecutively collected. From these radiographs, BA was determined using the Greulich and Pyle (GP) method by experts in a consensus manner; furthermore, BA was estimated by a previously reported artificial intelligence (AI) BA system based on Han children from southern China. The performance of the AI system was compared with that of experts by using statistical analysis. Results: Compared with the experts’ results, the accuracy of the AI system for Tibetan and Han children within 1 year was 84.67 and 89.41%, respectively, and its mean absolute difference (MAD) was 0.65 and 0.56 years, respectively. The discrepancy in hand-wrist bone maturation was the main cause of low accuracy of the system in the 4- to 6-year-old group. Conclusion: The AI BA system developed for Han Chinese children living in flat regions could enable to assess BA accurately in Tibet where medical resources are limited. Advances in knowledge: AI-based BA system may serve as an effective and efficient solution to assess BA in Tibet.

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