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Metaverse and Medical Diagnosis: A Blockchain-Based Digital Twinning Approach Based on MobileNetV2 Algorithm for

Omid Moztarzadeh^1,2, Mohammad Behdad Jamshidi³, Saleh Sargolzaei⁴

¹Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, 323 00 Pilsen, Czech Republic.

Diagnostics (Basel, Switzerland)

|May 16, 2023

Summary

This study introduces a blockchain-based metaverse platform for creating digital twins of dental issues, specifically cervical vertebral maturation (CVM). Deep learning with MobileNetV2 enables automated CVM diagnosis, enhancing dental care efficiency and accessibility.

Keywords:

CNN advanced mathematical models artificial intelligence deep learning dental surgery dentistry digital twins healthcare metaverse

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Area of Science:

* Digital Health and Medical Informatics
* Artificial Intelligence in Healthcare
* Dental Technology and Imaging

Background:

* Advanced deep learning (DL) algorithms are increasingly vital for medical diagnostics.
* Dentistry requires innovative solutions for diagnosis and treatment planning.
* Metaverse and blockchain technologies offer new paradigms for healthcare delivery.

Purpose of the Study:

* To design and implement a digital twin of cervical vertebral maturation (CVM) within a blockchain-based metaverse.
* To develop an automated CVM diagnosis process using DL and computer vision.
* To demonstrate the feasibility of low-cost digital twin creation for dental applications.

Main Methods:

* Development of a blockchain-based metaverse platform for dental digital twins.
* Implementation of a deep learning model, MobileNetV2, for automated CVM image analysis.
* Utilization of DL-based computer vision for real-time measurements, eliminating the need for additional sensors.

Main Results:

* Successful design and implementation of a digital twin for CVM on a blockchain metaverse.
* Automated diagnosis of CVM images using the MobileNetV2 DL model.
* Demonstrated high performance of the DL model on a small dataset, indicating suitability for real-world applications.

Conclusions:

* The proposed digital twin approach is simple, fast, and cost-effective for dental applications.
* This technology can be integrated with the Internet of Medical Things (IoMT) due to low latency and computing costs.
* Digital twins offer a viable solution for reducing diagnosis and treatment costs in dentistry.