Automated pathotyping of inflamed synovial tissue.
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30 Aug, 2024
Follow us on Researchers at Weill Cornell Medicine and Hospital for Special Surgery have developed a machine-learning tool that can identify different subtypes of rheumatoid arthritis. This could improve care for patients with this complex condition.
The study, published in Nature Communications, shows that AI can effectively analyze pathology samples from RA patients, potentially leading to better diagnosis and personalized treatment. Dr. Fei Wang noted that this tool could change how diseases are diagnosed and treated. While similar tools exist in oncology, Dr. Wang’s team is working to apply this technology to other medical fields.
Dr. Wang collaborated with Dr. Richard Bell and Dr. Lionel Ivashkiv to automate the process of identifying rheumatoid arthritis (RA) subtypes using tissue samples. This automation could help doctors choose the best therapy for each patient.
Pathologists manually classify arthritis subtypes by examining biopsy samples, which is slow, costly, and can lead to inconsistencies. Dr. Bell described it as a “bottleneck” in pathology research due to its time-consuming nature. A machine learning model can predict lymphocyte cells with high accuracy, shown in the blue and green overlays in the middle panel, on H&E histology slides. The predictions are confirmed with immunofluorescent staining of B cells (CD138 or CD20) and T cells (CD3). Credit: Adapted from Bell and Brindel, et al., 2024 The team trained their algorithm on RA samples from one group of mice to learn how to identify different tissue and cell types and sort them by subtype. They tested the tool on another set of samples, discovering new insights like reduced cartilage damage after six weeks of RA treatment.
They then used the tool on human biopsy samples and found it could accurately type them. Now, they’re validating the tool with more patient samples and exploring how to use it in routine pathology work.
Dr. Bell said “this tool is the first step towards more personalized RA care. It helps identify a patient’s subtype for quicker treatment. The technology can spot tissue changes humans might miss, reducing costs and improving efficiency in clinical trials for RA treatments.” Dr. Rainu […]
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