IIT Madras, CMC Vellore Build AI Tools for Early Kidney Disease Detection

Researchers at IIT Madras and Christian Medical College (CMC), Vellore, have developed a set of artificial intelligence (AI)-based tools designed to help detect and assess kidney diseases early, enabling timely intervention before significant damage occurs. The technologies include a machine learning model that uses clinical and laboratory data to predict the risk of chronic kidney disease (CKD), a deep learning system that automatically analyses computed tomography (CT) scans and sorts them into four categories — normal kidney, kidney cyst, kidney stone and kidney tumour — and a 3D imaging platform that recreates kidneys from CT scans to precisely assess tumour volume and the percentage of kidney involvement.

The CT image classifier was trained on more than 12,000 images and can distinguish healthy kidneys from cysts, stones and tumours. The 3D imaging framework, built using open-source software, offers a low-cost, repeatable way to assess tumour burden and could help doctors plan treatment.

According to G.L. Samuel, a professor in IIT Madras's Department of Mechanical Engineering, kidney diseases are often asymptomatic in their early stages and go undiagnosed until substantial damage has occurred. "These tools can reduce the need for expensive interventions like dialysis. We used machine learning along with clinical knowledge to develop tools that would assist in the earlier detection of kidney diseases and give more detailed information specific to the patient," Samuel said.

Jennifer Delighta, a research scholar at IIT Madras, said the chronic kidney disease model has been developed as a user-friendly prototype, with further work planned to improve its accuracy and interpretability for clinicians. Supported by IIT Madras and the SPARC research collaboration project, the study is also a step toward developing a kidney "digital twin." The researchers plan to validate the models using larger patient datasets and explore integrating them with wearable sensing systems for personalised kidney health monitoring.