학술논문
Field validation of deep learning based Point-of-Care device for early detection of oral malignant and potentially malignant disorders.
Document Type
Article
Author
Birur N., Praveen; Song, Bofan; Sunny, Sumsum P.; G., Keerthi; Mendonca, Pramila; Mukhia, Nirza; Li, Shaobai; Patrick, Sanjana; G., Shubha; A.R., Subhashini; Imchen, Tsusennaro; Leivon, Shirley T.; Kolur, Trupti; Shetty, Vivek; R., Vidya Bhushan; Vaibhavi, Daksha; Rajeev, Surya; Pednekar, Sneha; Banik, Ankita Dutta; Ramesh, Rohan Michael
Source
Subject
*DEEP learning
*EARLY detection of cancer
*MEDICAL screening
*ONCOLOGISTS
*POINT-of-care testing
*MAGNETIC resonance mammography
*ORAL cancer
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Language
ISSN
2045-2322
Abstract
Early detection of oral cancer in low-resource settings necessitates a Point-of-Care screening tool that empowers Frontline-Health-Workers (FHW). This study was conducted to validate the accuracy of Convolutional-Neural-Network (CNN) enabled m(mobile)-Health device deployed with FHWs for delineation of suspicious oral lesions (malignant/potentially-malignant disorders). The effectiveness of the device was tested in tertiary-care hospitals and low-resource settings in India. The subjects were screened independently, either by FHWs alone or along with specialists. All the subjects were also remotely evaluated by oral cancer specialist/s. The program screened 5025 subjects (Images: 32,128) with 95% (n = 4728) having telediagnosis. Among the 16% (n = 752) assessed by onsite specialists, 20% (n = 102) underwent biopsy. Simple and complex CNN were integrated into the mobile phone and cloud respectively. The onsite specialist diagnosis showed a high sensitivity (94%), when compared to histology, while telediagnosis showed high accuracy in comparison with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, however, when compared with telediagnosis, identified suspicious lesions with less sensitivity (60%). Phone integrated, CNN (MobileNet) accurately delineated lesions (n = 1416; sensitivity: 82%) and Cloud-based CNN (VGG19) had higher accuracy (sensitivity: 87%) with tele-diagnosis as reference standard. The results of the study suggest that an automated mHealth-enabled, dual-image system is a useful triaging tool and empowers FHWs for oral cancer screening in low-resource settings. [ABSTRACT FROM AUTHOR]