Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning and Virtual Patient Breathing Simulator using VR

@article{1715434,
      author = {Dr. D. Parameswari, Abinash S, Lingeshwaran N, Srihari M},
      title = {Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning and Virtual Patient Breathing Simulator using VR},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {2059-2065},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715434.pdf},
      abstract = {This paper presents two progressive phases of the med-real2sim project — an integrated framework for developing non-invasive medical digital twins in healthcare. phase 1 introduces a cardiac digital twin system that leverages physics-informed neural networks (pinns) and self-supervised learning to model patient cardiac profiles, simulate pressure–volume dynamics, and predict health outcomes from echocardiogram data. phase 2 extends the concept to respiratory physiology, presenting a virtual patient breathing simulator that employs webxr, webgl, and webassembly-based physics simulation to render immersive, browser-accessible 3d models of lung and diaphragm mechanics. together, the two phases demonstrate how digital twin technology, when combined with ai and physics-based modeling, can significantly improve diagnostic accuracy, personalize treatment, and enhance medical education through interactive virtual simulation.},
      keywords = {Digital Twin, Physics-Informed Neural Network, Medical AI, Cardiac Simulation, VR Healthcare, WebXR, Breathing Simulator, IoT Healthcare},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715434}
  }