Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning

Shinjini Kundu, Beth G. Ashinsky, Mustapha Bouhrara, Erik B. Dam, Shadpour Demehri, Mohammad Shifat-E-Rabbi, Richard G. Spencer, Kenneth L. Urish, Gustavo K. Rohde

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)24709-24719
Number of pages11
JournalProceedings of the National Academy of Sciences of the United States of America
Volume117
Issue number40
DOIs
StatePublished - Oct 6 2020

Keywords

  • 3D transport-based morphometry
  • Classification
  • Early diagnosis
  • Osteoarthritis
  • T2 imaging

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning'. Together they form a unique fingerprint.

Cite this