{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T23:15:04Z","timestamp":1785366904521,"version":"3.55.0"},"reference-count":117,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Mhealth Uhealth"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Resource limitations in public hospitals may hinder timely monitoring and management of rehabilitation in patients with nasopharyngeal carcinoma (NPC) after radiotherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study developed and evaluated the telemedicine app \u201cOpen Care,\u201d 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u20146 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The \u201cOpen Care\u201d 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial Registration<\/jats:title>\n                    <jats:p>Chinese Clinical Trial Registry ChiCTR2400090305; https:\/\/www.chictr.org.cn\/showprojEN.html?proj=235073<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/72560","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:20:09Z","timestamp":1770682809000},"page":"e72560","source":"Crossref","is-referenced-by-count":1,"title":["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 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