Researchers at the University of Houston have been awarded $3m to develop Artificial Intelligence systems for easier lupus nephritis diagnosis.
Current lupus nephritis diagnosis methods are fairly inaccurate and are marked by significant disagreement among pathologists reading biopsy reports.
This is where AI comes into the picture. AI systems combine computer science and robust datasets to enable problem-solving in healthcare.
The National Institute of Diabetes and Digestive and Kidney Diseases have now awarded $3m to the researchers to bring AI into the diagnostic picture.
What is lupus nephritis and why is it so difficult to diagnose?
Lupus nephritis is a perilous autoimmune disease that 60% of adults and 80% of children will develop after having systemic lupus erythematosus.
It occurs when the immune system wrongly attacks the kidneys, preventing them from doing jobs such as cleaning blood, balancing body fluids and controlling hormones that impact blood pressure.
Currently, the most precise method for lupus nephritis diagnosis faces many problems.
The kidney biopsy, which is a painful ordeal, reaches a tipping point when doctors must read the biopsy report.
“Given that this critical diagnostic step – which is important for planning treatment – is highly variable and imprecise, we sought out alternative approaches,” said Chandra Mohan, one of the researchers working on the project.
“This funding allows us to use Artificial Intelligence approaches to train a ‘neural network’ to learn how to read and classify lupus nephritis biopsy slides.”
How AI can significantly improve medical diagnosis
According to the researchers, the goal of using AI for lupus nephritis diagnosis will translate to better treatment for the condition.
The UH team will work closely with renal pathologists, including Jan Becker, Cologne, Germany; Luan Truong and Sadhna Dhingra, Houston Methodist; Qi Cai, UT Southwestern, Dallas, Texas; and Surya Seshan, Cornell University, Ithaca, New York.
This close collaboration will lead to diagnosis in an automated fashion with high accuracy.
Mohan explained: “By leveraging the power of computer vision and Deep Learning, a branch of Machine Learning, we will build classifiers that rival the best renal pathologists in making a diagnosis using current criteria.
“This could dramatically improve patient management and long-term renal and patient outcome.”
Hien Van Nguyen, associate professor of Electrical and Computer Engineering at UH, concluded: “This collaborative effort exemplifies how AI and medical expertise can intersect to drive innovation.
“I want to extend my gratitude to the hard-working team members who are committed to pushing the boundaries of what AI can do in the field of lupus nephritis.”
