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

• Author(s): Dr. D. Parameswari; Abinash S; Lingeshwaran N; Srihari M
• Paper ID: 1715434
• Page: 2059-2065
• Published Date: 25-03-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 9 March-2026
• DOI: https://doi.org/10.64388/IREV9I9-1715434

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

Citations

IRE Journals:
Dr. D. Parameswari, Abinash S, Lingeshwaran N, Srihari M "Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning and Virtual Patient Breathing Simulator using VR" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 2059-2065 https://doi.org/10.64388/IREV9I9-1715434

IEEE:
Dr. D. Parameswari, Abinash S, Lingeshwaran N, Srihari M "Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning and Virtual Patient Breathing Simulator using VR" Iconic Research And Engineering Journals, vol. 9, no. 9, Mar. 2026, doi: https://doi.org/10.64388/IREV9I9-1715434

APA:
Dr. D. Parameswari, Abinash S, Lingeshwaran N, Srihari M (2026). Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning and Virtual Patient Breathing Simulator using VR. Iconic Research And Engineering Journals, 9(9). doi: https://doi.org/10.64388/IREV9I9-1715434

MLA:
Dr. D. Parameswari, Abinash S, Lingeshwaran N, Srihari M "Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning and Virtual Patient Breathing Simulator using VR" Iconic Research And Engineering Journals, vol. 9, no. 9, Mar. 2026. Crossref, https://doi.org/10.64388/IREV9I9-1715434

BibTeX

@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}
}