A ‘transitional phase’ of applying AI techniques
“In a medical setting, as computer scientists, we face unique challenges,” pointed out Berend C. Stoel, MSc, PhD, the senior author of the Leiden study. “Our team consists of approximately 30-35 researchers, primarily electrical engineers or computer scientists, situated within the radiology department of Leiden University Medical Center. Our focus is on image processing, seeking AI-based solutions for image analysis, particularly utilizing deep learning techniques.”
Their objective is to validate this method more broadly, and to achieve that, they require collaboration with other hospitals. Up until now, they have primarily worked with a specific type of MR images, extremity MR scans. These scans are conducted in only a few centers equipped with extremity MR scanners, which can accommodate only hands or feet.
“We are currently in a transitional phase, aiming to apply our methods to standard MR scans, which are more widely available,” Dr. Stoel informed this news organization. “We are engaged in various projects. One project, nearing completion, involves the scoring of early RA, where we train the computer to imitate the actions of rheumatologists or radiologists. We started with a relatively straightforward approach, but AI offers a multitude of possibilities. In the project presented at EULAR, we manipulated the images in a different manner, attempting to predict future events. We also have a parallel project where we employ AI to detect inflammatory changes over time by analyzing sequences of images (MR scans). Furthermore, we have developed AI models to distinguish between treatment and placebo groups. Once the neural network has been trained for this task, we can inquire about the location and timing of changes, thereby gaining insights into the therapy’s response.
“When considering the history of AI, it has experienced both ups and downs. We are currently in a promising phase, but if certain projects fail, expectations might diminish. My hope is that we will indeed revolutionize and enhance disease diagnosis, monitoring, and prediction. Additionally, AI may provide us with additional information that we, as humans, may not be able to extract from these images. However, it is difficult to predict where we will stand in 5-10 years,” he concluded.
Predicting disease progression
The second study, which explored the application of AI in predicting the progression of undifferentiated arthritis (UA) to RA, was presented by Takayuki Fujii, MD, PhD, assistant professor in the department of advanced medicine for rheumatic diseases at Kyoto University’s Graduate School of Medicine in Japan. “Predicting the progression of RA from UA remains an unmet medical need,” he reminded the audience.
Dr. Takayuki Fujii
Dr. Takayuki Fujii
Dr. Fujii’s team used data from the KURAMA cohort, a large observational RA cohort from a single center, to develop a machine learning model. The study included a total of 322 patients initially diagnosed with UA. The deep neural network (DNN) model was trained using 24 clinical features that are easily obtainable in routine clinical practice, such as age, sex, C-reactive protein (CRP) levels, and disease activity score in 28 joints using erythrocyte sedimentation rate (DAS28-ESR). The DNN model achieved a prediction accuracy of 85.1% in the training cohort. When the model was applied to validation data from an external dataset consisting of 88 patients from the ANSWER cohort, a large multicenter observational RA cohort, the prediction accuracy was 80%.
“We have developed a machine learning model that can predict the progression of RA from UA using clinical parameters,” Dr. Fujii concluded. “This model has the potential to assist rheumatologists in providing appropriate care and timely intervention for patients with UA.”
“Dr. Fujii presented a fascinating study,” Dr. Jans said. “They achieved an accuracy of 80% when applying a DNN model to predict progression from UA to RA. This level of accuracy is relatively high and certainly promising. However, it is important to consider that a pre-test probability of 30% [for progressing from UA to RA] is also relatively high, which partially explains the high accuracy. Nonetheless, this study represents a significant step forward in the clinical management of patients with UA, as it helps identify those who may benefit the most from regular clinical follow-up.”
Dr. Li and Dr. Stoel report no relevant financial relationships with industry. Dr. Fujii has received speaking fees from Asahi Kasei, AbbVie, Chugai, and Tanabe Mitsubishi Pharma. Dr. Jans has received speaking fees from AbbVie, UCB, Lilly, and Novartis; he is cofounder of RheumaFinder. The Leiden study was funded by the Dutch Research Council and the China Scholarship Council. The study by Dr. Fujii and colleagues had no outside funding.
A version of this article first appeared on Medscape.com.