AI Article Synopsis
- A deep learning model called TDL-BAAM was developed to assess bone age using 15,129 pediatric hand radiographs and was compared to both automated methods and manual assessments done by radiologists using the Greulich and Pyle (GP) method.
- The TDL-BAAM model achieved a mean absolute error of 11.1 months, outperforming the GP-based model (12.9 months) and two pediatric radiologists (14.6 and 16.0 months).
- The study concluded that the deep learning approach is comparable to traditional methods for bone age assessment, highlighting its potential for further development tailored to specific populations.
Article Abstract
Purpose: To develop a deep learning approach to bone age assessment based on a training set of developmentally normal pediatric hand radiographs and to compare this approach with automated and manual bone age assessment methods based on Greulich and Pyle (GP).
Methods: In this retrospective study, a convolutional neural network (trauma hand radiograph-trained deep learning bone age assessment method [TDL-BAAM]) was trained on 15 129 frontal view pediatric trauma hand radiographs obtained between December 14, 2009, and May 31, 2017, from Children's Hospital of New York, to predict chronological age. A total of 214 trauma hand radiographs from Hasbro Children's Hospital were used as an independent test set. The test set was rated by the TDL-BAAM model as well as a GP-based deep learning model (GPDL-BAAM) and two pediatric radiologists (radiologists 1 and 2) using the GP method. All ratings were compared with chronological age using mean absolute error (MAE), and standard concordance analyses were performed.
Results: The MAE of the TDL-BAAM model was 11.1 months, compared with 12.9 months for GPDL-BAAM ( = .0005), 14.6 months for radiologist 1 ( < .0001), and 16.0 for radiologist 2 ( < .0001). For TDL-BAAM, 95.3% of predictions were within 24 months of chronological age compared with 91.6% for GPDL-BAAM ( = .096), 86.0% for radiologist 1 ( < .0001), and 84.6% for radiologist 2 ( < .0001). Concordance was high between all methods and chronological age (intraclass coefficient > 0.93). Deep learning models demonstrated a systematic bias with a tendency to overpredict age for younger children versus radiologists who showed a consistent mean bias.
Conclusion: A deep learning model trained on pediatric trauma hand radiographs is on par with automated and manual GP-based methods for bone age assessment and provides a foundation for developing population-specific deep learning algorithms for bone age assessment in modern pediatric populations.© RSNA, 2020See also the commentary by Halabi in this issue.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082327 | PMC |
| http://dx.doi.org/10.1148/ryai.2020190198 | DOI Listing |
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