Systemic lupus erythematosus (SLE) is a chronic autoimmune that can affect multiple major organ systems in the body. One of its most severe manifestations is renal (kidney) involvement, known as lupus nephritis (LN). Although LN can be detected via blood or urine tests, a kidney biopsy is considered the most precise diagnostic approach. However, there are challenges in interpreting biopsy reports due to discrepancies in pathologists’ interpretations. With research being undertaken in the US to leverage artificial intelligence (AI) to diagnose LN, there is a possibility of automating the detection of the disease, according to GlobalData, a leading data and analytics company.

In September 2023, Dr. Mohan and Dr. Van Nguyen, two faculty members from the University of Houston (UH) Cullen College of Engineering, were awarded a $3 million grant from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) to develop an AI program to aid in the diagnosis of LN. This funding enables the team to train a “neural network” to read and classify LN biopsy slides. Specifically, the research team intends to build a dedicated computer vision pipeline for classifying LN through the analysis of histopathology imaging data using machine learning (ML). Collaborating closely with renal pathologists, including experts from various institutions worldwide, the UH team aims to establish a computer-aided diagnosis system for LN, providing clinical decision support akin to renal pathologists.

The application of AI in the LN space was also researched in the past to assess the treatment responses to the disease. In 2021, a team of researchers at the Medical University of South Carolina (MUSC) introduced a pioneering machine learning (ML) algorithm tailored to forecast treatment responses in individuals with LN. This innovative model incorporated seven key disease indicators to predict the probability of a patient’s response to therapy within a year and showed promising outcomes.

Sravani Meka, Senior Immunology Analyst at GlobalData, comments: “As the prevalence of chronic diseases, such as LN, increases, so does the strain on the healthcare system. Due to multiple comorbidities associated with immunosuppressive treatment (e.g., infections, osteoporosis, cardiovascular (CV), and reproductive issues), LN patients often need other healthcare specialists.”

Given the broad multidisciplinary team required to effectively treat patients with LN and the increasing shortage of specialty doctors, it is not practical to assume that specialists, particularly rheumatologists and nephrologists, can keep up with the increasing volume of LN patients. However, it is possible that ML, a branch of AI, may prove to be a useful tool for improving medical diagnoses and alleviating the burden on the healthcare system.

Meka concludes: “As the AI program remains in its early development stages, it is not yet ready to be applied to the wider SLE population. Furthermore, as ML is still in its early adoption stages, not only in healthcare but in other industries, it remains to be seen when and how AI programs such as this one will be used more widely to deliver precise diagnoses, streamline clinical processes, reduce healthcare-related costs and the burden faced by specialists, and ultimately save lives.”