Open AccessA smart tele-cytology point-of-care platform for oral cancer screening
Author(s) -
Sumsum Sunny,
Arun Baby,
Bonney Lee James,
Dev Balaji,
N V Aparna,
Maitreya H. Rana,
Praveen Gurpur,
Arunan Skandarajah,
Michael V. D’Ambrosio,
Ravindra D. Ramanjinappa,
Sunil Paramel Mohan,
Nisheena Raghavan,
Uma Kandasarma,
N. Sangeetha,
Subhasini Raghavan,
Naveen Hedne,
Felix P. Koch,
Daniel A. Fletcher,
Sumithra Selvam,
Manohar Kollegal,
Praveen Birur,
Lance Ladic,
Amritha Suresh,
Hardik J. Pandya,
Moni Abraham Kuriakose
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0224885
Subject(s) - cytology , medicine , radiology , biopsy , point of care , risk stratification , pathology
Early detection of oral cancer necessitates a minimally invasive, tissue-specific diagnostic tool that facilitates screening/surveillance. Brush biopsy, though minimally invasive, demands skilled cyto-pathologist expertise. In this study, we explored the clinical utility/efficacy of a tele-cytology system in combination with Artificial Neural Network (ANN) based risk-stratification model for early detection of oral potentially malignant (OPML)/malignant lesion. A portable, automated tablet-based tele-cytology platform capable of digitization of cytology slides was evaluated for its efficacy in the detection of OPML/malignant lesions (n = 82) in comparison with conventional cytology and histology. Then, an image pre-processing algorithm was established to segregate cells, ANN was trained with images (n = 11,981) and a risk-stratification model developed. The specificity, sensitivity and accuracy of platform/ stratification model were computed, and agreement was examined using Kappa statistics. The tele-cytology platform, Cellscope, showed an overall accuracy of 84–86% with no difference between tele-cytology and conventional cytology in detection of oral lesions (kappa, 0.67–0.72). However, OPML could be detected with low sensitivity (18%) in accordance with the limitations of conventional cytology. The integration of image processing and development of an ANN-based risk stratification model improved the detection sensitivity of malignant lesions (93%) and high grade OPML (73%), thereby increasing the overall accuracy by 30%. Tele-cytology integrated with the risk stratification model, a novel strategy established in this study, can be an invaluable Point-of-Care (PoC) tool for early detection/screening in oral cancer. This study hence establishes the applicability of tele-cytology for accurate, remote diagnosis and use of automated ANN-based analysis in improving its efficacy.