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Automated Bone Age Analysis with Lossy Image Files.

Jordan E Pinsker1, Michael S L Ching2, Daniel P Chiles3

  • 1William Sansum Diabetes Center, 2219 Bath Street, Santa Barbara, CA 93105.

Military Medicine
|September 9, 2017
PubMed
Summary
This summary is machine-generated.

Automated bone age analysis using BoneXpert software is feasible and accurate for lossy JPEG files, improving access to pediatric endocrinology consultations. This technology enhances diagnostic capabilities for bone age assessment in remote military healthcare settings.

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Area of Science:

  • Radiology
  • Pediatric Endocrinology
  • Medical Informatics

Background:

  • Limited pediatric endocrinology and radiology resources for bone age assessment in the U.S. Department of Defense (DoD) hospital system.
  • Tertiary care center provides teleconsultation support to over 30 DoD facilities.
  • Bone age analysis is a common component of endocrinology teleconsultations.

Purpose of the Study:

  • Evaluate the feasibility of an automated bone age analysis program.
  • Assess the accuracy of automated analysis on lossy JPEG files.
  • Improve access to pediatric endocrinology consultation services.

Main Methods:

  • Utilized lossy JPEG bone age image files (70% quality) from a DoD hospital web viewer.
  • Employed BoneXpert software (version 2.1) with a custom automated system for image reconstruction.
  • Compared automated bone age readings and adult height predictions against pediatric endocrinologists and radiologists.

Main Results:

  • No significant differences in mean bone age readings between automated analysis and physicians (p = 0.827).
  • Mean differences in bone age readings were -0.09 years (vs. endocrinologists) and -0.03 years (vs. radiologists).
  • Mean difference in adult height prediction was -0.2 cm.

Conclusions:

  • Automated analysis of bone age using BoneXpert software on lossy JPEG files is feasible and accurate.
  • This approach can enhance the accessibility of specialized consultations.
  • Further research with larger studies is recommended for validation.