Researchers from the Indian Institute of Technology, Madras (IIT Madras), and Christian Medical College (CMC), Vellore, have developed a set of AI-based tools designed to assist in the early detection and assessment of kidney diseases, a press release said.
The team has developed three technologies that complement each other. The first is a machine learning model that uses clinical and laboratory information to predict the risk of chronic kidney disease (CKD). This CKD prediction model is implemented in a user-friendly prototype interface to facilitate future clinical translation. The research was led by G.L. Samuel, department of mechanical engineering, IIT Madras, and Jennifer Delighta, research scholar, IIT Madras, in collaboration with Santosh Varughese from the department of nephrology, CMC Vellore.
The research received institutional support from IIT Madras and the SPARC (Scheme for Promotion of Academic and Research Collaboration) project, the press release said.
Kidney diseases are often asymptomatic in their early stages and are not diagnosed until substantial damage has occurred. Since CKD can go unnoticed in its early stages, some patients are diagnosed only after considerable damage has occurred. In advanced cases, they may need dialysis, in which a machine filters the blood, or a kidney transplant.
The AI tools aim to assist physicians for earlier diagnosis, which could help slow the disease process and reduce the need for expensive interventions like dialysis. Earlier reporting noted: IIT-M, CMC develop AI tools to catch kidney disease early IIT-M, CMC develop AI tools to catch kidney disease early Earlier reporting noted: Chennai: Artificial intelligence tools that could help doctors assess kidney disease risk, interpret CT scans, and measure kidney tumours have been developed by researchers at IIT Madras in collaboration with doctors at Christian Medical College, Vellore. Earlier reporting noted: Chronic kidney disease, or CKD, is the loss of the organ’s ability to filter waste and excess fluid from the blood.
