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

  • Author(s): 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
  • et al.
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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