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Accepted for/Published in: JMIR mHealth and uHealth

Date Submitted: Feb 12, 2025
Date Accepted: Feb 9, 2026

The final, peer-reviewed published version of this preprint can be found here:

A Telemedicine App for Nonrigid Facial Rehabilitation Training Enhanced by Efficient Fully Convolutional Neural Network With Residual Network (EffiFCNN-ResNet) to Improve Accessibility for Patients With Nasopharyngeal Carcinoma Cancer: Randomized Controlled Trial

Wu T, Han T, Zhang X, Dai Y, Meng X

A Telemedicine App for Nonrigid Facial Rehabilitation Training Enhanced by Efficient Fully Convolutional Neural Network With Residual Network (EffiFCNN-ResNet) to Improve Accessibility for Patients With Nasopharyngeal Carcinoma Cancer: Randomized Controlled Trial

JMIR Mhealth Uhealth 2026;14:e72560

DOI: 10.2196/72560

PMID: 41805548

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Telemedicine Application for Non-Rigid Facial Rehabilitation Training Enhanced by EffiFCNN-ResNet to Improve Accessibility for Nasopharyngeal Carcinoma Cancer Patients:A Randomized Controlled Trial

  • Tong Wu; 
  • Ting Han; 
  • Xiaoju Zhang; 
  • Yumei Dai; 
  • Xiaoyan Meng

ABSTRACT

Background:

Resource limitations in public hospitals may hinder the timely monitoring and management of rehabilitation in patients with nasopharyngeal carcinoma (NPC) following radiotherapy.

Objective:

This study developed and evaluated the telemedicine application "Open Care," which integrates the EffiFCNN-ResNet model and computer vision to monitor and provide real-time feedback on facial training exercises, aiming to improve outcomes for patients with restricted mouth opening.

Methods:

A parallel-group, two-arm randomized controlled trial was conducted with 108 patients, randomly assigned to either the intervention group (n = 54) or the control group (n = 54).The intervention group performed mouth-opening exercises under the supervision and guidance of the telemedicine application, while the control group followed traditional video-based instructions.Primary outcome measures included maximum mouth opening, mouth opening symmetry, exercise frequency, and rehabilitation-related health beliefs. Secondary outcomes included fatigue (Brief Fatigue Inventory), health-related quality of life (AQoL-6D), and system usability (SUS). Data were analyzed using T-tests, chi-square tests, and Mann-Whitney U tests.

Results:

Results showed significant improvements in the intervention group in maximum mouth opening, exercise frequency, perceived benefits, self-efficacy, and action cues (p < 0.05). The system demonstrated 98.2% accuracy in assessing facial training exercises. Participants also reported favorable training experiences.

Conclusions:

This telemedicine approach was more effective than traditional methods, improving patient engagement and rehabilitation outcomes, while providing a more objective and precise monitoring tool. Future applications may benefit NPC and other head and neck cancer patients. Clinical Trial: Clinical trial registration:ChiCTR2400090305 Ethical approval:2404294-Exp8


 Citation

Please cite as:

Wu T, Han T, Zhang X, Dai Y, Meng X

A Telemedicine App for Nonrigid Facial Rehabilitation Training Enhanced by Efficient Fully Convolutional Neural Network With Residual Network (EffiFCNN-ResNet) to Improve Accessibility for Patients With Nasopharyngeal Carcinoma Cancer: Randomized Controlled Trial

JMIR Mhealth Uhealth 2026;14:e72560

DOI: 10.2196/72560

PMID: 41805548

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