Anders Christensen, MSc, Health and Welfare Technology, University of Southern Denmark, Odense, presented work on an automated scoring system for disease activity in RA patients using analysis of ultrasound images. The algorithm developed and trained by this group is a “convolutional neural network,” an arrangement of many layers of smart filters that, when correctly assembled and trained, can interpret and analyze complex data, such as medical images. The creation of a convolutional neural network is a supervised learning process, and the trainers of the computer are the rheumatologists who provide the ground truth (i.e., the correct outputs that the algorithm is supposed to produce). Mr. Christensen and colleagues note their system is a cascade of convolutional neural networks, which allows it to follow a hierarchy of decisions about the grade of synovial hypertrophy and synovitis to determine the correct, ultimate conclusion.
In evaluating more than 1,600 ultrasound images, the model achieved 84% accuracy. On a per patient level, no significant difference existed between the classifications of the computer model and the human experts. The research team indicated its data show convolutional neural networks with the cascade architecture can be used by rheumatologists to identify disease activity in RA patients.
In another presentation, Alarico Ariani, MD, from the Unit of Internal Medicine and Rheumatology, Azienda Ospedaliero Universitaria, Parma, Italy, described the use of a computer-assisted, automated quantitative computed tomography (QCT) system to evaluate interstitial lung disease in systemic sclerosis. In 2017, Dr. Ariani and colleagues showed the QCT method could discriminate between well-defined mortality risk categories, as well as clinical prediction models.5
Dr. Ariani aired a video showing how all needed clinical data about a patient could easily be entered into the QCT system to identify patients with a high risk of mortality within 10 years—all in less than one minute. This type of work in predicting mortality risk will play an essential role in determining which patients may benefit from new treatments, such as anti-fibrotic therapy.
More Advances for Rheumatology
The remainder of the presentations were equally compelling, describing the use of artificial intelligence to help with challenges in rheumatology. One group demonstrated the predictive value of bone texture features, extracted by deep-learning models, in the detection of osteoarthritis. Another research team described detection of subclinical skin manifestations in patients with psoriasis and psoriatic arthritis using fluorescence optical imaging, a technique that can evaluate changes in the microvasculature of the hands.
