Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability

• Published in: Skeletal radiology Vol. 48; no. 2; pp. 275 - 283
• Main Authors: Tajmir, Shahein H., Lee, Hyunkwang, Shailam, Randheer, Gale, Heather I., Nguyen, Jie C., Westra, Sjirk J., Lim, Ruth, Yune, Sehyo, Gee, Michael S., Do, Synho
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2019; Springer; Springer Nature B.V
• Subjects: Accuracy; Adolescent; Age; Age Determination by Skeleton - methods; Algorithms; Artificial Intelligence; Artificial neural networks; Bone Diseases, Metabolic - diagnostic imaging; Child; Child, Preschool; Coefficient of variation; Comparative analysis; Correlation analysis; Correlation coefficients; Data mining; Deep Learning; Error analysis; Female; Humans; Imaging; Machine learning; Male; Medicine; Medicine & Public Health; Metabolic disorders; Neural networks; Nuclear Medicine; Orthopedics; Pathology; Pediatrics; Performance enhancement; Radiographic Image Interpretation, Computer-Assisted - methods; Radiographs; Radiography; Radiology; Retrospective Studies; Scientific Article; Augmented intelligence; Pediatric; Bone age; Radiographs; Machine learning
• ISSN: 0364-2348; 1432-2161; 1432-2161
• DOI: 10.1007/s00256-018-3033-2

Objective Radiographic bone age assessment (BAA) is used in the evaluation of pediatric endocrine and metabolic disorders. We previously developed an automated artificial intelligence (AI) deep learning algorithm to perform BAA using convolutional neural networks. We compared the BAA performance of a cohort of pediatric radiologists with and without AI assistance. Materials and methods Six board-certified, subspecialty trained pediatric radiologists interpreted 280 age- and gender-matched bone age radiographs ranging from 5 to 18 years. Three of those radiologists then performed BAA with AI assistance. Bone age accuracy and root mean squared error (RMSE) were used as measures of accuracy. Intraclass correlation coefficient evaluated inter-rater variation. Results AI BAA accuracy was 68.2% overall and 98.6% within 1 year, and the mean six-reader cohort accuracy was 63.6 and 97.4% within 1 year. AI RMSE was 0.601 years, while mean single-reader RMSE was 0.661 years. Pooled RMSE decreased from 0.661 to 0.508 years, all individually decreasing with AI assistance. ICC without AI was 0.9914 and with AI was 0.9951. Conclusions AI improves radiologist’s bone age assessment by increasing accuracy and decreasing variability and RMSE. The utilization of AI by radiologists improves performance compared to AI alone, a radiologist alone, or a pooled cohort of experts. This suggests that AI may optimally be utilized as an adjunct to radiologist interpretation of imaging studies to improve performance.