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01-02-2019 | Scientific Article

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

Authors: Shahein H. Tajmir, Hyunkwang Lee, Randheer Shailam, Heather I. Gale, Jie C. Nguyen, Sjirk J. Westra, Ruth Lim, Sehyo Yune, Michael S. Gee, Synho Do

Published in: Skeletal Radiology | Issue 2/2019

Abstract

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.

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Title: Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability
Authors: Shahein H. Tajmir
Hyunkwang Lee
Randheer Shailam
Heather I. Gale
Jie C. Nguyen
Sjirk J. Westra
Ruth Lim
Sehyo Yune
Michael S. Gee
Synho Do
Publication date: 01-02-2019
Publisher: Springer Berlin Heidelberg
Published in: Skeletal Radiology / Issue 2/2019
Print ISSN: 0364-2348
Electronic ISSN: 1432-2161
DOI: https://doi.org/10.1007/s00256-018-3033-2

At a glance: The STEP trials

Springer Medicine

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

Abstract

Objective

Materials and methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Objective

Materials and methods

Results

Conclusions

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