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Bone age estimation from chest radiographs using deep neural networks: a proof-of-concept study
Hidehito Ota1, Takaya Hanawa2, Hiroshi Yoshihara3
1Department of Pediatrics, University of Tokyo Hospital, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8655, Japan. hioota-tky@umin.ac.jp.
Insights
Deep neural networks can estimate bone age from chest X-rays, offering a potential alternative when hand X-rays are unavailable. This AI approach aids pediatric growth assessment.
Area of Science:
- Pediatric radiology
- Artificial intelligence in medicine
- Skeletal maturity assessment
Background:
- Bone age (BA) assessment is crucial for pediatric growth evaluation but relies on hand radiographs, which are not always practical.
- Current methods require specialized equipment and expert interpretation, limiting routine use.
Purpose of the Study:
- To develop a deep neural network model for estimating bone age from standard pediatric chest radiographs.
- To assess the feasibility of using AI for opportunistic bone age estimation.
Main Methods:
- Retrospective analysis of 101 children (3-15 years) with both chest and hand radiographs.
- Exclusion of patients with skeletal dysplasia or chest wall abnormalities.
- Development and fine-tuning of deep neural networks using sex-considering and sex-non-considering models to estimate BA from chest X-rays, validated against the Tanner-Whitehouse 2 radius-ulna-short bones (TW2-RUS) method.
Main Results:
- The deep learning models demonstrated good concordance with reference bone age standards.
- Sex-considering models achieved higher accuracy, with intraclass correlation coefficients (ICCs) up to 0.87 and root mean squared errors (RMSEs) as low as 1.30.
- Sex-non-considering models showed comparable results with ICCs up to 0.81 and RMSEs of 1.52.
Conclusions:
- Deep neural networks can accurately estimate bone age from pediatric chest radiographs.
- This AI-driven approach presents a viable, opportunistic alternative to dedicated hand radiographs for skeletal maturity assessment.
- Further validation could integrate this method into routine pediatric imaging workflows.
Background:
Bone age (BA) is the gold standard for skeletal maturity assessment but is not routinely incorporated into pediatric growth evaluation workflow because it requires dedicated hand radiographs and specialist interpretation.
Objective:
To develop an estimation model for BA from chest radiographs using deep neural networks.
Materials And Methods:
We retrospectively analyzed children aged 3-15 years who underwent both chest and hand radiography at a tertiary center over 20 years. Patients with skeletal dysplasia or chest wall abnormalities were excluded. Reference BA was determined from hand radiographs by pediatric endocrinologists using the Tanner-Whitehouse 2 radius-ulna-short bones (TW2-RUS) method. Three pretrained deep neural networks were fine-tuned to estimate BA from chest radiographs using sex-non-considering models and sex-considering models. Model performance was evaluated using the intraclass correlation coefficient (ICC), root mean squared error (RMSE), and related metrics.
Results:
Of 180 screened patients, 101 were included, yielding 237 chest radiographs. Estimated BA showed good concordance with the reference standard, with ICCs up to 0.81 (95% CI 0.48-0.97) for sex-non-considering models and 0.87 (95% CI 0.63-0.99) for sex-considering models. Corresponding RMSEs were 1.52 (95% CI 0.67-2.20) and 1.30 (95% CI 0.56-1.95), respectively.
Conclusion:
This proof-of-concept study demonstrates the feasibility of estimating BA from pediatric chest radiographs using deep neural networks. These findings suggest potential opportunistic use when dedicated hand radiographs are unavailable.