{
  "id": 5298786,
  "title": "IIT Madras and CMC Vellore researchers build AI tools for early kidney disease detection",
  "url": "https://urgent.news/2026/09/03/iit-madras-and-cmc-vellore-researchers-build-ai-tools-for-early-5298786",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-03T09:57:52.000Z",
  "source": {
    "name": "The Hindu - Sci-Tech",
    "slug": "the-hindu-sci-tech",
    "url": "https://www.thehindu.com/news/cities/chennai/iit-madras-and-cmc-vellore-researchers-build-ai-tools-for-early-kidney-disease-detection/article71422950.ece"
  },
  "original_language": "en",
  "account": "Researchers from IIT Madras and CMC Vellore have collaborated to create a trio of AI-powered tools aimed at early detection and assessment of kidney diseases, according to a press release. The team developed three complementary technologies, each designed to work seamlessly together. The first tool is a machine learning model that uses clinical and laboratory data to determine the risk of chronic kidney disease (CKD). This prediction model is integrated into a user-friendly interface to aid future clinical applications. The second tool is a deep learning system that automatically analyzes CT scans, categorizing them into four distinct conditions: normal kidney, kidney cyst, kidney stone, and kidney tumor. Trained on over 12,000 images, the image classifier can accurately differentiate between healthy kidneys and those exhibiting cysts, stones, or tumors. Lastly, the team developed a 3D imaging platform that reconstructs kidney structures from CT scans, enabling precise assessment of tumor volume and the extent of kidney involvement. This 3D imaging framework was created using open-source software and represents a significant step towards developing a kidney 'digital twin'. This digital twin would integrate AI-assisted image analysis with patient-specific 3D anatomical models, facilitating personalized clinical decision-making. Kidney diseases often remain asymptomatic in their early stages and are typically diagnosed only after significant damage has occurred. The AI tools aim to assist physicians in detecting these conditions earlier, potentially slowing the disease progression and reducing the need for costly interventions such as dialysis. The research, led by G.L. Samuel from IIT Madras' mechanical engineering department and Jennifer Delighta, a research scholar at IIT Madras, was conducted in collaboration with Santosh Varughese from CMC Vellore's nephrology department. The project received support from IIT Madras and the SPARC (Scheme for Promotion of Academic and Research Collaboration) program.",
  "summary": "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",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "The Hindu Health",
        "title": "IIT Madras and CMC Vellore researchers build AI tools for early kidney disease detection",
        "url": "https://urgent.news/2026/09/03/iit-madras-and-cmc-vellore-researchers-build-ai-tools-for-early",
        "published": "2026-09-03T09:57:52.000Z"
      }
    ]
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}