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Artificial intelligence, osteoporosis and fragility fractures

Ferizi, Urana; Honig, Stephenb; Chang, Gregorya

Current Opinion in Rheumatology: July 2019 - Volume 31 - Issue 4 - p 368–375
doi: 10.1097/BOR.0000000000000607
METABOLIC BONE DISEASE: Edited by Stephen Honig

Purpose of review Artificial intelligence tools have found new applications in medical diagnosis. These tools have the potential to capture underlying trends and patterns, otherwise impossible with previous modeling capabilities. Machine learning and deep learning models have found a role in osteoporosis, both to model the risk of fragility fracture, and to help with the identification and segmentation of images.

Recent findings Here we survey the latest research in the artificial intelligence application to the prediction of osteoporosis that has been published between January 2017 and March 2019. Around half of the articles that are covered here predict (by classification or regression) an indicator of osteoporosis, such as bone mass or fragility fractures; the other half of studies use tools for automatic segmentation of the images of patients with or at risk of osteoporosis. The data for these studies include diverse signal sources: acoustics, MRI, CT, and of course, X-rays.

Summary New methods for automatic image segmentation, and prediction of fracture risk show promising clinical value. Though these recent developments have had a successful initial application to osteoporosis research, their development is still under improvement, such as accounting for positive/negative class bias. We urge care when reporting accuracy metrics, and when comparing such metrics between different studies.

aDepartment of Radiology

bDivision of Rheumatology, Department of Medicine, New York University School of Medicine, USA

Correspondence to Uran Ferizi, PhD, Department of Radiology, New York University School of Medicine, 660 First Avenue, 4th floor, New York, NY 10016, USA. Tel: +1 212 263 3398; e-mail:

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