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

TL;DR

A telemedicine app using a neural network model improves facial rehabilitation for nasopharyngeal cancer patients by providing real-time feedback and better outcomes than traditional methods.

Contribution

The novel EffiFCNN-ResNet model enables real-time monitoring and feedback for nonrigid facial rehabilitation exercises in a telemedicine app.

Findings

The telemedicine app significantly improved maximum mouth opening, exercise frequency, and health beliefs compared to traditional methods.

The model showed strong generalization (F1-score 0.96) and clinical stability (5.2% performance degradation in challenging conditions).

Users reported high usability scores (mean 74.3/100) and improved fatigue and quality of life outcomes.

Abstract

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

This study developed and evaluated the telemedicine app “Open Care,” which integrates the Efficient Fully Convolutional Neural Network with Residual Network (EffiFCNN-ResNet) model and computer vision to monitor facial training exercises and provide real-time feedback, aiming to improve outcomes in patients with restricted mouth opening.

Initially, the EffiFCNN-ResNet model underwent 5-fold cross-validation, expert validation, and robustness testing to assess its reliability and clinical applicability in complex real-world environments. Subsequently, to evaluate the telemedicine app, a parallel-group, 2-arm randomized controlled trial was conducted with 109 patients, who were randomly assigned to either the intervention group (n=55) or the control group (n=54). The intervention group performed mouth-opening exercises under the supervision and guidance of the telemedicine app, whereas 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 (Assessment of Quality of Life—6 Dimensions), and system usability scores. Data were analyzed using 2-tailed (unpaired) independent-samples t tests and chi-square tests, and the Mann-Whitney U test was used to assess intra- and inter-group differences before and after the intervention.

The “Open Care” system leverages a lightweight fully convolutional neural network (FCNN) depth model integrated with network communication to enable real-time capture, recognition, and correction of nonrigid facial training movements. It also provides visual feedback and supports automated rehabilitation assessment. The model demonstrated strong generalization ability (macro-averaged F1-score, mean 0.96, SD 0.01) and clinical-grade stability (performance degradation: mean 5.2%, SD 0.6%, under lighting disturbances and challenging pathological cases; n=160 video segments). Compared with the control group, the intervention group showed significant improvements in maximum mouth opening (P=.04), exercise frequency (P=.001), perceived severity (P=.007), perceived benefits (P=.04), perceived barriers (P=.001), self-efficacy (P=.04), cues to action (P=.001), health behavior (P=.03), and fatigue (P=.04). Participants also reported favorable training experiences, with a mean system usability score of 74.3 out of 100.

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 apps may benefit patients with NPC and other head and neck cancers.

Chinese Clinical Trial Registry ChiCTR2400090305; https://www.chictr.org.cn/showprojEN.html?proj=235073

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