globalchange  > 气候变化与战略
DOI: 10.1073/pnas.1917405117
论文题名:
Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning
作者: Kundu S.; Ashinsky B.G.; Bouhrara M.; Dam E.B.; Demehri S.; Shifat-E-Rabbi M.; Spencer R.G.; Urish K.L.; Rohde G.K.
刊名: Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
出版年: 2020
卷: 117, 期:40
起始页码: 24709
结束页码: 24719
语种: 英语
英文关键词: 3D transport-based morphometry ; Classification ; Early diagnosis ; Osteoarthritis ; T2 imaging
Scopus关键词: articular cartilage ; cohort analysis ; diagnostic imaging ; disease exacerbation ; early diagnosis ; female ; human ; knee osteoarthritis ; machine learning ; male ; nuclear magnetic resonance imaging ; pathology ; Cartilage, Articular ; Cohort Studies ; Disease Progression ; Early Diagnosis ; Female ; Humans ; Machine Learning ; Magnetic Resonance Imaging ; Male ; Osteoarthritis, Knee
英文摘要: 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. © 2020 National Academy of Sciences. All rights reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163385
Appears in Collections:气候变化与战略

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作者单位: Kundu, S., Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States, Medical Scientist Training Program, University of Pittsburgh, Pittsburgh, PA 15261, United States; Ashinsky, B.G., Laboratory of Clinical Investigation, Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, United States; Bouhrara, M., Laboratory of Clinical Investigation, Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, United States; Dam, E.B., Department of Computer Science, University of Copenhagen, Copenhagen, 2100, Denmark; Demehri, S., Department of Radiology, Johns Hopkins Hospital, Baltimore, MD 21287, United States; Shifat-E-Rabbi, M., Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, United States; Spencer, R.G., Laboratory of Clinical Investigation, Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, United States; Urish, K.L., Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States, Arthritis and Arthroplasty Design Group, Bone and Joint Center, Magee Womens Hospital of the University of Pittsburgh Medical Center, Pittsburgh, PA 15213, United States, Department of Orthopedic Surgery, University of Pittsburgh, Pittsburgh, PA 15213, United States, Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15261, United States, Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA 15261, United States; Rohde, G.K., Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, United States, Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22908, United States

Recommended Citation:
Kundu S.,Ashinsky B.G.,Bouhrara M.,et al. Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning[J]. Proceedings of the National Academy of Sciences of the United States of America,2020-01-01,117(40)
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