biostudies-literatureUnknown5(8)Sun D<h4>Importance</h4>An automated, accurate method is needed for unbiased assessment quantifying accrual of joint space narrowing and erosions on radiographic images of the hands and wrists, and feet for clinical trials, monitoring of joint damage over time, assisting rheumatologists with treatment decisions. Such a method has the potential to be directly integrated into electronic health records.<h4>Objectives</h4>To design and implement an international crowdsourcing competition to catalyze the development of machine learning methods to quantify radiographic damage in rheumatoid arthritis (RA).<h4>Design, setting, and participants</h4>This diagnostic/prognostic study describes the Rheumatoid Arthritis 2-Dialogue for Reverse Engineering Assessment and Methods (RA2-DREAM Challenge), which usJAMA network opene2227423https://www.ebi.ac.uk/biostudies/studies/S-EPMC9425151biostudies-literatureA Crowdsourcing Approach to Develop Machine Learning Models to Quantify Radiographic Joint Damage in Rheumatoid Arthritis.PMC9425151Benadit JQuek RShreemali AMason MXue CNguyen-Ba KTBiehl ADimitrovsky IWang JFrazier MBBai RRA2-DREAM Challenge CommunityVaithinathan KAllaway RJChilukuri SSun DStadler MNguyen TMCostello JCWu YLi HOlar AChung VPataki BATran DSuomi TNguyen TVenalainen MSEricson LWojna ZGulko PSIsrael AChaturvedi NGuan YKrason ABridges SLShi CPietila STan YHe XChen JYMahmoudian MElo LLGuinney JYu TVRyu JJStolovitzky GfalseA Crowdsourcing Approach to Develop Machine Learning Models to Quantify Radiographic Joint Damage in Rheumatoid Arthritis.<h4>Importance</h4>An automated, accurate method is needed for unbiased assessment quantifying accrual of joint space narrowing and erosions on radiographic images of the hands and wrists, and feet for clinical trials, monitoring of joint damage over time, assisting rheumatologists with treatment decisions. Such a method has the potential to be directly integrated into electronic health records.<h4>Objectives</h4>To design and implement an international crowdsourcing competition to catalyze the development of machine learning methods to quantify radiographic damage in rheumatoid arthritis (RA).<h4>Design, setting, and participants</h4>This diagnostic/prognostic study describes the Rheumatoid Arthritis 2-Dialogue for Reverse Engineering Assessment and Methods (RA2-DREAM Challenge), which us2022-01-01T00:00:00Z2022 Aug2025-04-26T13:41:34.721Z2025-02-19T00:47:45.725ZS-EPMC94251513603693510.1001/jamanetworkopen.2022.27423