Journal article
Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, v 117(40), pp 24709-24719
06 Oct 2020
PMID: 32958644
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Many diseases have no visual cues in the early stages, eluding image-based detection. Today, osteoarthritis (OA) is detected after bone damage has occurred, at an irreversible stage of the disease. Currently no reliable method exists for OA detection at a reversible stage. We present an approach that enables sensitive OA detection in presymptomatic individuals. Our approach combines optimal mass transport theory with statistical pattern recognition. Eighty-six healthy individuals were selected from the Osteoarthritis Initiative, with no symptoms or visual signs of disease on imaging. On 3-y follow-up, a subset of these individuals had progressed to symptomatic OA. We trained a classifier to differentiate progressors and nonprogressors on baseline cartilage texture maps, which achieved a robust test accuracy of 78% in detecting future symptomatic OA progression 3 y prior to symptoms. This work demonstrates that OA detection may be possible at a potentially reversible stage. A key contribution of our work is direct visualization of the cartilage phenotype defining predictive ability as our technique is generative. We observe early biochemical patterns of fissuring in cartilage that define future onset of OA. In the future, coupling presymptomatic OA detection with emergent clinical therapies could modify the outcome of a disease that costs the United States healthcare system $16.5 billion annually. Furthermore, our technique is broadly applicable to earlier image-based detection of many diseases currently diagnosed at advanced stages today.
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Details
- Title
- Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning
- Publication Details
- PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, v 117(40), pp 24709-24719
- Publisher
- NATL ACAD SCIENCES; WASHINGTON
- Number of pages
- 0
- Grant note
- G.K.R. and M.S.-E.-R. were supported in part by NIH Awards GM130825 and GM090033. This work was supported in part by the NIH, National Institute on Aging, Intramural Research Program. K.L.U. is supported in part by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (K08AR071494), the National Center for Advancing Translational Science (KL2TR0001856), the Orthopedic Research and Education Foundation, and the Musculoskeletal Tissue Foundation.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000579059100022
- Scopus ID
- 2-s2.0-85092680930
- Other Identifier
- 991021860679904721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Orthopedics