In a significant health care breakthrough, researchers at Weill Cornell Medicine and the Hospital for Special Surgery have unveiled an AI-powered tool capable of subtyping rheumatoid arthritis, paving the way for more personalized treatments.
In a groundbreaking advancement, researchers at Weill Cornell Medicine and the Hospital for Special Surgery (HSS) have developed an innovative machine-learning tool that can identify subtypes of rheumatoid arthritis (RA). Promising a new era of personalized medicine, this tool has the potential to transform RA diagnosis and treatment, as detailed in their study published in Nature Communications.
Led by Fei Wang, a professor of population health sciences at Weill Cornell Medicine and founding director of the Institute of AI for Digital Health, the study showcases the tool’s ability to automate the analysis of pathology slides.
“Our tool automates the analysis of pathology slides, which may one day lead to more precise and efficient disease diagnosis and personalized treatment for RA,” Wang said in a news release. “It shows that machine learning can potentially transform pathological assessment of many diseases.”
Addressing a Labor-Intensive Process
Currently, pathologists manually classify RA subtypes by examining cell and tissue characteristics in biopsy samples — a cumbersome and slow process. This new technology aims to speed up and streamline this task, reducing inconsistencies and overall costs.
Wang’s collaboration with Richard Bell, a postdoctoral associate in medicine at Weill Cornell Medical College, and Lionel Ivashkiv, a professor of medicine and immunology at Weill Cornell Medicine and chief scientific officer at HSS, led to the automation of RA tissue sample subtyping.
By training an algorithm on samples from mice and validating it with further samples, they demonstrated that the tool could guide treatment choices more effectively.
“It’s the analytical bottleneck of pathology research,” said Bel in the news release. “It is very time-consuming and tedious.”
Validating and Applying the Tool
After successful trials with animal samples, the researchers extended their efforts to patient biopsy samples from the Accelerating Medicines Partnership Rheumatoid Arthritis research consortium.
The results suggest that this tool could revolutionize clinical workflows by efficiently subtyping human RA samples, ultimately improving the speed and accuracy of diagnosis and treatment.
“It’s the first step towards more personalized RA care,” Bell added. “If you can build an algorithm that identifies a patient’s subtype, you’ll be able to get patients the treatments they need more quickly.”
Towards a New Era in Personalized Medicine
The tool’s implications extend beyond RA, with potential to be adapted for other diseases like osteoarthritis, disc degeneration and tendinopathy.
The researchers are also exploring its application in broader biomedical contexts, having recently demonstrated its efficacy in subtyping Parkinson’s disease.
“By integrating pathology slides with clinical information, this tool demonstrates AI’s growing impact in advancing personalized medicine,” added Rainu Kaushal, senior associate dean for clinical research and chair of the Department of Population Health Sciences at Weill Cornell Medicine. “This research is particularly exciting as it opens new pathways for detection and treatment, making significant strides in how we understand and care for people with rheumatoid arthritis.”
Future Prospects
As the researchers continue to validate this innovative tool with additional patient samples, the integration of such technology into everyday clinical practices could become a pivotal moment in personalized health care.
According to Ivashkiv, this advancement not only represents an important leap in RA tissue analysis but also holds immense potential for benefitting patients across various complex conditions.
“This work represents an important advance in analyzing RA tissues that can be applied for the benefit of patients,” Ivashkiv concluded.
With their eyes set on the future, the team aims to inspire more computational research in the field, hoping to see machine learning tools tailored for a wider array of diseases.
“We hope our research will trigger more computational research on developing machine learning tools for more diseases,” said Wang.