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 |
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| Hlavní autoři: | , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Public Library of Science
05.12.2018
Public Library of Science (PLoS) |
| Témata: | |
| ISSN: | 1932-6203, 1932-6203 |
| On-line přístup: | Získat plný text |
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| Abstract | 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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| AbstractList | 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%. 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%.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%. |
| Audience | Academic |
| Author | Kuriakose, Moni Abraham Birur, Praveen Uthoff, Ross D. Spires, Oliver Keerthi, G. Patrick, Sanjana Sunny, Sumsum Anbarani, Afarin Kolur, Trupti Wilder-Smith, Petra Suresh, Amritha Liang, Rongguang Song, Bofan |
| AuthorAffiliation | 4 Biocon Foundation, Bangalore, India 5 KLE Society’s Institute of Dental Sciences, Bangalore, India 1 College of Optical Sciences, The University of Arizona, Tucson, Arizona, United States of America 2 Mazumdar Shaw Medical Centre, Bangalore, India 6 Beckman Laser Institute, University of California, Irvine, Irvine, California, United States of America 3 Mazumdar Shaw Medical Foundation, Bangalore, India Texas A&M University, UNITED STATES |
| AuthorAffiliation_xml | – name: 3 Mazumdar Shaw Medical Foundation, Bangalore, India – name: 2 Mazumdar Shaw Medical Centre, Bangalore, India – name: 4 Biocon Foundation, Bangalore, India – name: 6 Beckman Laser Institute, University of California, Irvine, Irvine, California, United States of America – name: 5 KLE Society’s Institute of Dental Sciences, Bangalore, India – name: 1 College of Optical Sciences, The University of Arizona, Tucson, Arizona, United States of America – name: Texas A&M University, UNITED STATES |
| Author_xml | – sequence: 1 givenname: Ross D. orcidid: 0000-0003-1002-5546 surname: Uthoff fullname: Uthoff, Ross D. – sequence: 2 givenname: Bofan orcidid: 0000-0002-1171-8478 surname: Song fullname: Song, Bofan – sequence: 3 givenname: Sumsum surname: Sunny fullname: Sunny, Sumsum – sequence: 4 givenname: Sanjana surname: Patrick fullname: Patrick, Sanjana – sequence: 5 givenname: Amritha orcidid: 0000-0002-0785-0054 surname: Suresh fullname: Suresh, Amritha – sequence: 6 givenname: Trupti surname: Kolur fullname: Kolur, Trupti – sequence: 7 givenname: G. orcidid: 0000-0003-4594-8674 surname: Keerthi fullname: Keerthi, G. – sequence: 8 givenname: Oliver surname: Spires fullname: Spires, Oliver – sequence: 9 givenname: Afarin surname: Anbarani fullname: Anbarani, Afarin – sequence: 10 givenname: Petra surname: Wilder-Smith fullname: Wilder-Smith, Petra – sequence: 11 givenname: Moni Abraham surname: Kuriakose fullname: Kuriakose, Moni Abraham – sequence: 12 givenname: Praveen surname: Birur fullname: Birur, Praveen – sequence: 13 givenname: Rongguang surname: Liang fullname: Liang, Rongguang |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30517120$$D View this record in MEDLINE/PubMed |
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| Copyright | COPYRIGHT 2018 Public Library of Science 2018 Uthoff et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2018 Uthoff et al 2018 Uthoff et al |
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| SubjectTerms | Applications programs Artificial neural networks Bioengineering Cancer Cancer screening Classification Cloud Computing Computer and Information Sciences Data processing Diagnosis Diagnostic tests Early Detection of Cancer - instrumentation Early Detection of Cancer - methods Electronic mail systems Engineering and Technology Fluorescence Health care Health care costs Health risks Humans Image classification LEDs Lesions Light emitting diodes Low income groups Medical diagnosis Medical economics Medical imaging Medical imaging equipment Medical screening Medicine and Health Sciences Methyltestosterone Mobile Applications Morbidity Mortality Mouth cancer Mouth Neoplasms - diagnosis Neural networks Neural Networks, Computer Optical Imaging Optics Oral cancer Oral cavity Point of care testing Point-of-Care Systems Poverty Remote regions Research funding Security devices Sensitivity and Specificity Smart phones Smartphone - instrumentation Smartphones Spectrum analysis Tobacco White light |
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| Title | Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities |
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