Early CKD detection: An AI model analyses clinical and laboratory data to identify patients at risk of chronic kidney disease.
Smarter kidney imaging: A deep-learning system classifies CT scans into normal kidney, cyst, stone, and tumor categories.
Towards a Kidney Digital Twin: Patient-specific 3D kidney models could help doctors measure tumors, monitor disease, and personalise treatment.
Kidney disease can stay hidden for a long time. Many patients show no clear signs at an early stage, so doctors may find the problem only after serious damage. A new project from IIT Madras and Christian Medical College (CMC), Vellore, aims to close that gap with three AI tools. The systems can assess chronic kidney disease risk, read kidney CT scans, and create 3D kidney models for tumor checks.
The first tool uses clinical and laboratory data to predict the risk of chronic kidney disease, or CKD. It gives doctors an early risk signal from patient data. The team has also placed the CKD model inside a simple prototype interface. Researchers now seek better accuracy and clearer results that doctors can understand with ease.
The second tool focuses on CT scans. A deep-learning system sorts kidney scans into four groups: normal kidney, kidney cyst, kidney stone, and kidney tumor. The team trained the image classifier with more than 12,000 images. This system can help doctors review scans faster and get a more consistent first assessment.
The third tool adds a 3D view of the kidney. It uses CT scans to create a patient-specific kidney model. The platform can measure tumor volume and the share of the kidney affected by the disease. The team built the framework with open-source software, which offers a lower-cost and repeatable way to assess tumor burden.
The three tools form part of a larger plan. The researchers want to create a kidney Digital Twin, a virtual model that can reflect key details of a real patient’s kidney. Such a system could combine AI image analysis with a patient-specific 3D model. Doctors could then use the data to monitor disease, assess changes, and plan care for an individual patient.
The idea goes beyond a single scan or one test result. A Digital Twin could place several forms of patient data into one view. The team also wants to link the technology with wearable sensors for long-term kidney health checks. A future sensor patch could assess body fluids and offer a signal about disease progress, Prof. G.L. Samuel said in a recent report.
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Prof. G.L. Samuel from the Department of Mechanical Engineering at IIT Madras leads the research with Jennifer Delighta, a research scholar at IIT Madras. Prof. Santosh Varughese from the Department of Nephrology at CMC Vellore works with the team.
The project received institutional support from IIT Madras and the SPARC Scheme for Promotion of Academic and Research Collaboration. The research aims to give doctors faster and more detailed information so they can make better clinical decisions at an earlier stage.
The current work remains at a research stage. The team plans to test the models with more patient datasets and seek stronger links with healthcare institutions for real-world use. The latest announcement does not give a final accuracy rate for the full set of tools.
A recent report also highlighted a key challenge: India lacks a large open patient-record repository that researchers can easily use for AI development. The team has sought more data from hospitals and other health institutions. Prof. Samuel also said routine hospital use could still take at least five years.
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The strength of the project lies in its three-part approach. One tool checks CKD risk from clinical data. Another classifies kidney conditions from CT scans. The third measures tumor size and kidney involvement through a 3D model. Together, the tools cover risk assessment, image review, and detailed disease measurement.
Early diagnosis can give doctors more time to act before kidney damage becomes severe. The project does not yet offer a ready clinical product, but it sets a clear path from AI-based risk checks to patient-specific kidney models. Its next test will come from larger datasets, wider clinical validation, and real hospital use.
1. What are IIT Madras and CMC Vellore developing?
They are developing three AI tools for CKD risk prediction, kidney CT scan classification, and 3D kidney tumor assessment.
2. How can AI help detect kidney disease early?
AI can analyse clinical data and medical images to identify potential risks or abnormalities before symptoms become obvious.
3. What can the CT scan AI tool detect?
The system classifies kidney CT images into four categories: normal kidney, cyst, kidney stone, and kidney tumor.
4. What is a Kidney Digital Twin?
It is a patient-specific virtual kidney model that could combine imaging and other health data to help doctors monitor disease and plan care.
5. Is the technology ready for routine hospital use?
Not yet. The tools require further validation using larger patient datasets and real-world clinical testing, and routine use could still take several years.