A Crowdsourcing Approach to Develop Machine Learning Models to Quantify Radiographic Joint Damage in Rheumatoid Arthritis

IMPORTANCE: 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...

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Detalles Bibliográficos
Autores principales: Sun, Dongmei, Nguyen, Thanh M., Allaway, Robert J., Wang, Jelai, Chung, Verena, Yu, Thomas V., Mason, Michael, Dimitrovsky, Isaac, Ericson, Lars, Li, Hongyang, Guan, Yuanfang, Israel, Ariel, Olar, Alex, Pataki, Balint Armin, Stolovitzky, Gustavo, Guinney, Justin, Gulko, Percio S., Frazier, Mason B., Chen, Jake Y., Costello, James C., Bridges, S. Louis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2022
Materias:
Original Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9425151/
https://www.ncbi.nlm.nih.gov/pubmed/36036935
http://dx.doi.org/10.1001/jamanetworkopen.2022.27423

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9425151/
https://www.ncbi.nlm.nih.gov/pubmed/36036935
http://dx.doi.org/10.1001/jamanetworkopen.2022.27423

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