Bone age assessment
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At the time the article was created Caron Parsons had no recorded disclosures.View Caron Parsons's current disclosures
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Bone age assessment is used to radiologically assess the biological and structural maturity of immature patients from their hand and wrist x-ray appearances. It forms an important part of the diagnostic and management pathway in children with growth and endocrine disorders. It is helpful in the diagnosis of various growth disorders and can provide a prediction of final height for patients presenting with short stature.
Bone age can also be used to monitor children on growth hormone therapy or those presenting in delayed or advanced stages of puberty that may need treatment.
Assessment is performed with a radiograph of the non-dominant hand with a single DP view that includes the distal radius and ulna and all the fingers. Appearances of the carpal bones, metacarpal, phalanges, radius, and ulna are compared to standardized versions in one of two main atlases:
- Greulich and Pyle atlas presents a single standardized image for a range of ages of each gender 2
- Tanner-Whitehouse (TW) method involves the scoring of each carpal bone, the radius and ulna leading to a total score, from which age can be estimated 3
In addition, software tools are available to automate the task of bone age assessment. In 2017, the RSNA held a machine learning challenge to automate bone age assessment. The winning model achieved a mean absolute difference from the gold-standard (the average of a panel of pediatric radiologists) of 4.265 months 4.
- 1. Vicente Gilsanz, Osman Ratib. Bone Age Atlas. (2005) ISBN: 9783540209515 - Google Books
- 2. William Walter Greulich, Sarah Idell Pyle. Radiographic Atlas of Skeletal Development of the Hand and Wrist. (1999) ISBN: 9780804703987 - Google Books
- 3. James Mourilyan Tanner. Assessment of Skeletal Maturity and Prediction of Adult Height. (2001) ISBN: 9780702025112 - Google Books
- 4. Halabi S, Prevedello L, Kalpathy-Cramer J et al. The RSNA Pediatric Bone Age Machine Learning Challenge. Radiology. 2019;290(2):498-503. doi:10.1148/radiol.2018180736 - Pubmed