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Machine learning and detection of osteoarthritis

09.21.20 | Proceedings of the National Academy of Sciences

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In a study of 86 people with no discernible symptoms of osteoarthritis, retrospective analysis of cartilage texture maps by a machine learning classifier found that the classifier detected the beginning stages of osteoarthritis progression with 78% accuracy up to 3 years before symptom onset, suggesting that early detection may enable treatment at a stage when damage is reversible, according to the authors.

Article #19-17405: "Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning," by Shinjini Kundu et al.

MEDIA CONTACT: Shinjini Kundu, Johns Hopkins Hospital, Baltimore, MD; e-mail: skundu2@jhmi.edu

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Proceedings of the National Academy of Sciences

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Shinjini Kundu
skundu2@jhmi.edu

How to Cite This Article

APA:
Proceedings of the National Academy of Sciences. (2020, September 21). Machine learning and detection of osteoarthritis. Brightsurf News. https://www.brightsurf.com/news/1GRDG758/machine-learning-and-detection-of-osteoarthritis.html
MLA:
"Machine learning and detection of osteoarthritis." Brightsurf News, Sep. 21 2020, https://www.brightsurf.com/news/1GRDG758/machine-learning-and-detection-of-osteoarthritis.html.