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Atlas-based Machine Learning for Pediatric Bone Age Assessment

Award Number: R21HD119531
ORGANIZATION: NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 06/01/2026
PERIOD OF PERFORMANCE END DATE: 05/31/2028

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Atlas-based Machine Learning for Pediatric Bone Age Assessment - Project Summary Bone age assessment (BAA) for evaluating skeletal maturation is performed exclusively during childhood. It is typically based on x-ray images of the left hand and wrist. It is widely used in general pediatrics and pediatric endocrinology as a safe and cost-effective tool for diagnosis, treatment planning, and prognosis, with minimal radiation risk. In the United States (U.S.), about 4.7% of the children need BAA. In the U.S., the Greulich-Pyle (GP) method is widely used for BAA, and it consists of a set of manual interpretation guidelines and two x-ray image Atlases (i.e., male and female GP Atlases) of White children in Cleveland, Ohio. Following the GP method, a radiologist (or an endocrinologist) memorizes the GP atlases and the corresponding interpretation guidelines as much as possible. The radiologist/endocrinologist then estimates a child’s bone age by visually comparing the child’s hand and wrist x-ray with the reference x-ray images in the GP Atlases, following the interpretation guidelines. Due to the subjective nature of human judgment and the qualitative nature of the guidelines, interreader variability in bone age estimation can be as high as 5.8 months. Another known issue is that the GP method causes significant misinterpretation of bone ages in Non-White children (e.g., Asian and Hispanic children) because the GP Atlases only have data of White children and the guidelines were created based on such data. Consequently, it is imperative to develop a new BAA method with more comprehensive atlases and quantitative guidelines. Machine learning (ML) for BAA has gained considerable attention due to its potential to automate the BAA process. However, the current ML methods for BAA rely on data annotated by humans with the GP method. As a result, these ML methods cannot perform better than the GP method, and the deficiencies of the GP method are inherited by the ML methods developed on the GP method-derived training data. Also, the current ML methods for BAA use end-to-end trained “black-box” models, making it difficult for human users to understand and verify decisions from ML, which limits their acceptance in clinical practice. In this project, we will develop a novel Atlas-based ML approach for BAA, and it will overcome the limitations of the above methods and therefore achieve better performance by major advantages: (1) intuitive to and verifiable by humans, and (2) reconfigurable for use with appropriate atlases (better than the GP Atlases) for different racial/ethnical groups. This approach will be developed and validated on internal and external datasets.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $395,006 )
2026	2026	UNIVERSITY OF MIAMI	1320 SOUTH DIXIE HIGHWAY STE 650	CORAL GABLES	FL	33146	MIAMI-DADE	USA	Child Health and Human Development Extramural Research	000	1	5/27/2026	NEW	$395,006
														Subtotal = $395,006

Grand Total All Awards = $395,006

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Atlas-based Machine Learning for Pediatric Bone Age Assessment

Award Number: R21HD119531

ORGANIZATION: NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 06/01/2026

PERIOD OF PERFORMANCE END DATE: 05/31/2028

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