Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities

Oral cancer is a growing health issue in a number of low- and middle-income countries (LMIC), particularly in South and Southeast Asia. The described dual-modality, dual-view, point-of-care oral cancer screening device, developed for high-risk populations in remote regions with limited infrastructur...

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Vydáno v:PloS one Ročník 13; číslo 12; s. e0207493
Hlavní autoři: Uthoff, Ross D., Song, Bofan, Sunny, Sumsum, Patrick, Sanjana, Suresh, Amritha, Kolur, Trupti, Keerthi, G., Spires, Oliver, Anbarani, Afarin, Wilder-Smith, Petra, Kuriakose, Moni Abraham, Birur, Praveen, Liang, Rongguang
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 05.12.2018
Public Library of Science (PLoS)
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ISSN:1932-6203, 1932-6203
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Shrnutí:Oral cancer is a growing health issue in a number of low- and middle-income countries (LMIC), particularly in South and Southeast Asia. The described dual-modality, dual-view, point-of-care oral cancer screening device, developed for high-risk populations in remote regions with limited infrastructure, implements autofluorescence imaging (AFI) and white light imaging (WLI) on a smartphone platform, enabling early detection of pre-cancerous and cancerous lesions in the oral cavity with the potential to reduce morbidity, mortality, and overall healthcare costs. Using a custom Android application, this device synchronizes external light-emitting diode (LED) illumination and image capture for AFI and WLI. Data is uploaded to a cloud server for diagnosis by a remote specialist through a web app, with the ability to transmit triage instructions back to the device and patient. Finally, with the on-site specialist's diagnosis as the gold-standard, the remote specialist and a convolutional neural network (CNN) were able to classify 170 image pairs into 'suspicious' and 'not suspicious' with sensitivities, specificities, positive predictive values, and negative predictive values ranging from 81.25% to 94.94%.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0207493