Along with imaging, AI and machine learning are being looked at for an array of potential applications within rheumatology, such as disease detection and stratification, prediction of disease flares, prediction of disease progression and use of genetic biomarkers to personalize treatment.
Our department can:
- Assist radiologists and rheumatologists in improving efficient, accurate diagnosis. Example: AI analysis of musculoskeletal diseases is relatively easy given the binary questions asked (e.g., Are osteophytes present or not? Is joint space narrowing present or not? Is there calcification in the meniscus or not?). This is the type of repetitive task that provides an inroad to AI and machine learning;
- Generate automated reports, reducing the time clinicians spend on dictating radiologic reports, such as dual-energy X-ray absorptiometry findings, and providing more time for difficult cases or interacting with patients;
- Reduce the number of images needed for patients by automatically detecting and identifying features in images taken for other purposes. Example: An abdominal CT scan performed for other reasons may automatically provide data on bone density; and
- Allow faster, better, large-scale scoring of images for clinical trials, for example in osteoarthritis. Automated image analysis will eliminate variability among researchers.