Chennai: Researchers from IIT Madras and Christian Medical College (CMC), Vellore, have developed a set of Artificial Intelligence (AI)-based tools designed to assist in the early detection and assessment of kidney diseases, which affect millions of people worldwide, officials said.
The three new technologies are – a machine learning model that uses clinical and laboratory information to predict the risk of chronic kidney disease (CKD); a deep learning system that automatically analyses CT scans and classifies 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.
According to GL Samuel, Professor at the Department of Mechanical Engineering, IIT Madras, kidney diseases are often asymptomatic in their early stages and are not diagnosed until substantial damage has occurred. The AI tools aim to assist physicians by delivering consistent results quickly, especially in a fast-paced healthcare environment.
“These tools can enable earlier diagnosis, which could help slow down the disease process and reduce the need for expensive interventions like dialysis. The team aimed to develop intelligent systems that would help clinicians make quicker and more informed decisions.
“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.
The CT image classifier has been trained with more than 12,000 images and can distinguish healthy kidneys from cysts, stones and tumours. The 3D imaging framework developed using open-source software provides an inexpensive and repeatable method for measuring tumour burden, which can provide valuable information to help guide treatment decisions.
Jennifer Delighta, research scholar at IIT Madras, emphasised that early detection is of paramount importance when dealing with kidney diseases.
“These AI tools can help detect at-risk patients early and plan their treatment more effectively. The patient-specific imaging framework is of significant promise as it goes beyond the standard measurements to give a more comprehensive picture of the extent of the disease,” Delighta said.
The CKD prediction model was implemented in a user-friendly prototype interface to facilitate future clinical translation. The team also worked on making it more accurate and more easily understood by the doctors who are using it to make their predictions.
The study represents an important step towards the development of a kidney Digital Twin, integrating AI-assisted image analysis with patient-specific 3D anatomical models for personalised clinical decision-making, where virtual twins of patients’ organs may be applied to monitor, forecast and plan individual therapy.
“The team intends to test the models with more patient information sets to validate them and establish stronger partnerships with health care institutions for deployment in the real world. The researchers are also exploring the long-term integration of these AI technologies with minimally invasive wearable sensing systems and Digital Twin platforms to enable personalised kidney health monitoring,” Samuel said.
