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Evidence

Application of Machine Learning in the Diagnosis of Axial Spondyloarthritis

Date: 07/16/2019

Journal: Current Opinion in Rheumatology

Date published: April 2019

Authors: Walsh, Jessica A; Rozycki, Martin; Yi, Esther; Park, Yujin

Objectives:

  • Describe the development and application of machine-learning models in the field of rheumatology to improve the detection and diagnosis rates of underdiagnosed rheumatologic conditions, such as ankylosing spondylitis and axial spondyloarthritis (axSpA).

Conclusions:

Machine-learning algorithms may have a substantial role in medical diagnosis, especially in underrecognized diseases, such as axSpA. Machine-learning models that account for clinical significance appear to be the most promising; however, it will be important to explain its limitations in addition to the opportunities for healthcare providers and patients. A timely diagnosis of axSpA may be possible with this analytic approach, but further refinements will be needed to optimize its operability and ability to correctly distinguish patients with ankylosing spondylitis/axSpA from the general population. As more applications employ these machine-learning techniques, we must not overlook the need to consider potential ethical and regulatory issues.

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