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Skeletal Radiology

, Volume 48, Issue 2, pp 275–283 | Cite as

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

  • 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
Scientific Article

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.

Keywords

Machine learning Bone age Augmented intelligence Radiographs Pediatric 

Notes

Compliance with ethical standards

Conflict of interest

None

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Copyright information

© ISS 2018

Authors and Affiliations

  • Shahein H. Tajmir
    • 1
    • 2
  • Hyunkwang Lee
    • 1
    • 3
  • Randheer Shailam
    • 1
    • 2
  • Heather I. Gale
    • 4
  • Jie C. Nguyen
    • 5
  • Sjirk J. Westra
    • 1
    • 2
  • Ruth Lim
    • 1
    • 2
  • Sehyo Yune
    • 1
    • 2
  • Michael S. Gee
    • 1
    • 2
  • Synho Do
    • 1
    • 2
  1. 1.Department of RadiologyMassachusetts General HospitalBostonUSA
  2. 2.Harvard Medical SchoolBostonUSA
  3. 3.Harvard John A. Paulson School of Engineering and Applied SciencesCambridgeUSA
  4. 4.The Billings ClinicBillingsUSA
  5. 5.Children’s Hospital of PhiladelphiaPhiladelphiaUSA

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