Sunny, S and Baby, A and James, BL and Balaji, D and Aparna, NV and Rana, MH and Gurpur, P and Skandarajah, A and D�Ambrosio, M and Ramanjinappa, RD and Mohan, SP and Raghavan, N and Kandasarma, U and Sangeetha, N and Raghavan, S and Hedne, N and Koch, F and Fletcher, DA and Selvam, S and Kollegal, M and Praveen Birur, N and Ladic, L and Suresh, A and Pandya, HJ and Kuriakose, MA (2019) A smart tele-cytology point-of-care platform for oral cancer screening. In: PLoS ONE, 14 (11).
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Abstract
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 telecytology for accurate, remote diagnosis and use of automated ANN-based analysis in improving its efficacy. © 2019 Sunny et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
| Item Type: | Journal Article |
|---|---|
| Publication: | PLoS ONE |
| Publisher: | Public Library of Science |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | adult; algorithm; Article; artificial neural network; cancer grading; cancer risk; cancer screening; cohort analysis; controlled study; cytology; demography; diagnostic accuracy; female; histopathology; human; image processing; image quality; major clinical study; male; mouth squamous cell carcinoma; point of care testing; population research; scoring system; sensitivity and specificity; tele cytology point of care platform; cytodiagnosis; early cancer diagnosis; middle aged; mouth tumor; point of care system; procedures; risk assessment; telemedicine, Algorithms; Cytodiagnosis; Early Detection of Cancer; Female; Humans; Image Processing, Computer-Assisted; Male; Middle Aged; Mouth Neoplasms; Neural Networks, Computer; Point-of-Care Systems; Risk Assessment; Sensitivity and Specificity; Telemedicine |
| Department/Centre: | Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology) |
| Date Deposited: | 11 Oct 2022 11:19 |
| Last Modified: | 11 Oct 2022 11:19 |
| URI: | https://eprints.iisc.ac.in/id/eprint/77422 |
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