Field validation of deep learning based Point-of-Care device for early detection of oral malignant and potentially malignant disorders.
Sci Rep
; 12(1): 14283, 2022 08 22.
Article
em En
| MEDLINE
| ID: mdl-35995987
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.
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Neoplasias Bucais
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Telemedicina
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Telefone Celular
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Aprendizado Profundo
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article