Authors:
Anuj Kumar, Satya Prakash Yadav, Awadhesh Kumar

Addresses:
Department of Computer Science and Engineering, Dr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow, Uttar Pradesh, India. Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India. Department of Computer Science and Engineering, Kamala Nehru Institute of Technology, Sultanpur, Uttar Pradesh, India. 

Abstract:

Interest is surging among technology companies and industries with respect to the idea of what has come to be called the “metaverse”: A “virtual” (or digital) world where humans may interact with one another in real-time, as well as participate in activities. Many have begun to explore how the concept may affect the future development of many areas. For instance, the medical imaging industry has also begun to examine how to leverage the potential benefits of the Metaverse, where individuals may experience what it might be like to see and interpret a medical image. The adoption of medical imaging in the Metaverse could still benefit greatly from greater standardization and the development of workflows for integrating deep learning (DL) into this new environment. The authors develop an improved DL framework by combining established DL strategies with VR technologies, thereby improving both efficiency and accuracy for processing medical images. They specifically highlight how incorporating VR headsets into medical image processing enables presentation of medical image data via a 3D virtual environment. The technology engages healthcare providers and uses 3D medical imaging data to improve data analysis. The model had 97.11% accuracy, 98.32% precision, 98.66% recall, and 95.60% F1-score. Pre-existing deep learning networks can be quickly adapted for virtual world applications using transfer learning and modular neural networks. This will reduce the time and cost of creating metaverse-specific models.

Keywords: Medical Professionals; Virtual Environment; Rapid Adaptation; Neural Networks; Healthcare Technology; Comprehensive Solution; Model Accuracy; Data Analysis; Digital Health.

Received on: 14/04/2025, Revised on: 19/06/2025, Accepted on: 11/09/2025, Published on: 07/05/2026

DOI: 10.69888/FTSHSL.2026.000635

FMDB Transactions on Sustainable Health Science Letters, 2026 Vol. 4 No. 2, Pages: 82-101

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