MILAN – Two independent efforts to use artificial intelligence (AI) to predict the development of early rheumatoid arthritis (RA) from patients with signs and symptoms not meeting full disease criteria showed good, near expert-level accuracy, according to findings from two studies presented at the annual European Congress of Rheumatology.
In one study, researchers from Leiden University Medical Center in the Netherlands developed an AI-based method to automatically analyze MR scans of extremities in order to predict early rheumatoid arthritis. The second study involved a Japanese research team that used machine learning to create a model capable of predicting progression from undifferentiated arthritis (UA) to RA. Both approaches would facilitate early diagnosis of RA, enabling timely treatment and improved clinical outcomes.
Dr. Lennart Jans
Dr. Lennart Jans
Lennart Jans, MD, PhD, who was not involved in either study but works with AI-assisted imaging analysis on a daily basis as head of clinics in musculoskeletal radiology at Ghent University Hospital and a professor of radiology at Ghent University in Belgium, said that integrating AI into health care poses several challenging aspects that need to be addressed. “There are three main challenges associated with the development and implementation of AI-based tools in clinical practice,” he said. “Firstly, obtaining heterogeneous datasets from different image hardware vendors, diverse racial and ethnic backgrounds, and various ages and genders is crucial for training and testing the AI algorithms. Secondly, AI algorithms need to achieve a predetermined performance level depending on the specific use case. Finally, a regulatory pathway must be followed to obtain the necessary FDA or MDR [medical devices regulation] certification before applying an AI use case in clinical practice.”
RA prediction
Yanli Li, the first author of the study and a member of the division of image processing at Leiden University Medical Center, explained the potential benefits of early RA prediction. “If we could determine whether a patient presenting with clinically suspected arthralgia (CSA) or early onset arthritis (EAC) is likely to develop RA in the near future, physicians could initiate treatment earlier, reducing the risk of disease progression.”
Currently, rheumatologists estimate the likelihood of developing RA by visually scoring MR scans using the RAMRIS scoring system. “We decided to explore the use of AI,” Dr. Li explained, “because it could save time, reduce costs and labor, eliminate the need for scoring training, and allow for hypothesis-free discoveries.”
The research team collected MR scans of the hands and feet from Leiden University Medical Center’s radiology department. The dataset consisted of images from 177 healthy individuals, 692 subjects with CSA (including 113 who developed RA), and 969 with EAC (including 447 who developed RA). The images underwent automated preprocessing to remove artifacts and standardize the input for the computer. Subsequently, a deep learning model was trained to predict RA development within a 2-year time frame.
The training process involved several steps. Initially, the researchers pretrained the model to learn anatomy by masking parts of the images and tasking the computer with reconstructing them. Subsequently, the AI was trained to differentiate between the groups (EAC vs. healthy and CSA vs. healthy), then between RA and other disorders. Finally, the AI model was trained to predict RA.
The accuracy of the model was evaluated using the area under the receiver operator characteristic curve (AUROC). The model that was trained using MR scans of the hands (including the wrist and metacarpophalangeal joints) achieved a mean AUROC of 0.84 for distinguishing EAC from healthy subjects and 0.83 for distinguishing CSA from healthy subjects. The model trained using MR scans of both the hands and feet achieved a mean AUROC of 0.71 for distinguishing RA from non-RA cases in EAC. The accuracy of the model in predicting RA using MR scans of the hands was 0.73, which closely matches the reported accuracy of visual scoring by human experts (0.74). Importantly, the generation and analysis of heat maps suggested that the deep learning model predicts RA based on known inflammatory signals.
“Automatic RA prediction using AI interpretation of MR scans is feasible,” Dr. Li said. “Incorporating additional clinical data will likely further enhance the AI prediction, and the heat maps may contribute to the discovery of new MRI biomarkers for RA development.”
“AI models and engines have achieved near-expertise levels for various use cases, including the early detection of RA on MRI scans of the hands,” said Dr. Jans, the Ghent University radiologist. “We are observing the same progress in AI detection of rheumatic diseases in other imaging modalities, such as radiography, CT, and ultrasound. However, it is important to note that the reported performances often apply to selected cohorts with standardized imaging protocols. The next challenge [for Dr. Li and colleagues, and others] will be to train and test these algorithms using more heterogeneous datasets to make them applicable in real-world settings.”