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

Autor: Ankita Dutta Banik, Shubha G, Rohan Ramesh, Sneha Pednekar, Praveen Birur N, Trupti Kolur, Amritha Suresh, Petra Wilder Smith, Surya Rajeev, Bofan Song, Nirza Mukhia, Shaobai Li, Vidya Bushan R, Vijay Pillai, Moni Abraham Kuriakose, Alben Sigamani, Tsusennaro Imchen, Shubhashini Ar, Sanjana Patrick, Rongguang Liang, Pramila Mendonca, Kathryn Osann, Daksha Vaibhavi, Sumsum P. Sunny, Vivek Shetty, Keerthi G, Shirley T Leivon
Rok vydání: 2021
Předmět:
Zdroj: SSRN Electronic Journal.
ISSN: 1556-5068
Popis: 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.
Databáze: OpenAIRE