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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doaj-afe2e415d61d496183481eec6f465e072021-03-03T21:04:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011312e020749310.1371/journal.pone.0207493Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities.Ross D UthoffBofan SongSumsum SunnySanjana PatrickAmritha SureshTrupti KolurG KeerthiOliver SpiresAfarin AnbaraniPetra Wilder-SmithMoni Abraham KuriakosePraveen BirurRongguang LiangOral 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%.https://doi.org/10.1371/journal.pone.0207493 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ross D Uthoff Bofan Song Sumsum Sunny Sanjana Patrick Amritha Suresh Trupti Kolur G Keerthi Oliver Spires Afarin Anbarani Petra Wilder-Smith Moni Abraham Kuriakose Praveen Birur Rongguang Liang |
spellingShingle |
Ross D Uthoff Bofan Song Sumsum Sunny Sanjana Patrick Amritha Suresh Trupti Kolur G Keerthi Oliver Spires Afarin Anbarani Petra Wilder-Smith Moni Abraham Kuriakose Praveen Birur Rongguang Liang Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. PLoS ONE |
author_facet |
Ross D Uthoff Bofan Song Sumsum Sunny Sanjana Patrick Amritha Suresh Trupti Kolur G Keerthi Oliver Spires Afarin Anbarani Petra Wilder-Smith Moni Abraham Kuriakose Praveen Birur Rongguang Liang |
author_sort |
Ross D Uthoff |
title |
Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. |
title_short |
Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. |
title_full |
Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. |
title_fullStr |
Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. |
title_full_unstemmed |
Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. |
title_sort |
point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2018-01-01 |
description |
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%. |
url |
https://doi.org/10.1371/journal.pone.0207493 |
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