mHealth-Based Point-Of-Care Diagnostic Tool for Early Detection of Oral Cancer and Pre-Cancer Lesions in a Low-Resource Setting

Background: Early detection of oral cancer in low-resource settings necessitates a robust, Point-of-Care screening tool that can empower Frontline-Health-Workers (FHW) for triaging high-risk populations. This study was conducted to validate the accuracy of Artificial-Neural-Network (ANN) enabled m(mobile)-Health device deployed with FHWs. Methods: The dual-imaging device enables white-light as well as auto-fluorescence imaging with both a wide and focused view. The effectiveness of the device was tested in tertiary-care/dental hospitals and low-resource settings of South/North-East India. Demographics and clinical details were documented in the device, and the subjects were screened independently, either by FHWs or along with specialists, in addition to a remote evaluation by oral cancer specialists. Simple and complex ANN were built using the images for classification of oral-potentially-malignant/malignant lesions and integrated into the mobile phone/cloud. The specialist diagnoses were compared with the histopathology, FHW, and ANN-based diagnosis to evaluate the efficacy of the oral cancer detection program. Findings: The program screened 5025 subjects with 95% (n=4728) having tele-diagnosis. Among the 16% (n=752) assessed by onsite specialists, biopsy was advised for 515, out of which 20% (n=102) underwent biopsy. The onsite specialist diagnosis showed high sensitivity (94%) and moderate specificity (72%) when compared to histology diagnosis, while tele-diagnosis showed high accuracy when compared with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, when compared with tele-diagnosis, identified suspicious lesions with less sensitivity (60%). Further, the efficacy of the FHWs in delineating suspicious lesions correlated (r= 0·80) with their prior experience in the project. The ANN (MobileNet) integrated with the phone for a real-time diagnosis could accurately delineate lesions (n=1416; sensitivity: 82%), while the cloud-based ANN (VGG19) had higher accuracy (sensitivity: 87%) when compared to tele-diagnosis. Interpretation: This study suggests that an automated mHealth-enabled dual-image system could prove a useful triaging tool, empowering FHWs towards accurate screening in low-resource settings. Clinical Trial: The study protocol was registered in the Clinical Trial Registry of the Indian Council of Medical Research (CTRI/2019/11/022167). Funding: This study is primarily funded by the National Institutes of Health (NIH), USA grants (UH2EB022623, UH3CA239682). This work is also supported by LAMMP NIH/NIBIB P41EB05890, Arnold and Mabel Beckman Foundation, California Tobacco-Related Diseases Program: T31IR1825. Declaration of Interest: We declare no competing interests. Ethical Approval: Institutional Ethics Committee approvals were obtained from the three nodal centers- The KLE Society’s Institute of Dental Sciences (KLE; ECR/887/Inst/KA/2016), Bengaluru, India, Christian Institute of Health Sciences and Research (CIHSR; EC/NEW/INST/2020/782), Dimapur, Nagaland, India, and Mazumdar Shaw Medical Center (MSMC; NNH/MEC-CL-2016-394), Bengaluru, India prior to the initiation of the clinical trial.