Validation of a Point-of-Care Optical Coherence Tomography Device with Machine Learning Algorithm for Detection of Oral Potentially Malignant and Malignant Lesions.

Non-invasive strategies that can identify oral malignant and dysplastic oral potentially-malignant lesions (OPML) are necessary in cancer screening and long-term surveillance. Optical coherence tomography (OCT) can be a rapid, real time and non-invasive imaging method for frequent patient surveillance. Here, we report the validation of a portable, robust OCT device in 232 patients (lesions: 347) in different clinical settings. The device deployed with algorithm-based automated diagnosis, showed efficacy in delineation of oral benign and normal (n = 151), OPML (n = 121), and malignant lesions (n = 75) in community and tertiary care settings. This study showed that OCT images analyzed by automated image processing algorithm could distinguish the dysplastic-OPML and malignant lesions with a sensitivity of 95% and 93%, respectively. Furthermore, we explored the ability of multiple (n = 14) artificial neural network (ANN) based feature extraction techniques for delineation high grade-OPML (moderate/severe dysplasia). The support vector machine (SVM) model built over ANN, delineated high-grade dysplasia with sensitivity of 83%, which in turn, can be employed to triage patients for tertiary care. The study provides evidence towards the utility of the robust and low-cost OCT instrument as a point-of-care device in resource-constrained settings and the potential clinical application of device in screening and surveillance of oral cancer.

A smart tele-cytology point-of-care platform for oral cancer screening.

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.

Mobile microscopy as a screening tool for oral cancer in India: A pilot study.

Oral cancer is the most common type of cancer among men in India and other countries in South Asia. Late diagnosis contributes significantly to this mortality, highlighting the need for effective and specific point-of-care diagnostic tools. The same regions with high prevalence of oral cancer have seen extensive growth in mobile phone infrastructure, which enables widespread access to telemedicine services. In this work, we describe the evaluation of an automated tablet-based mobile microscope as an adjunct for telemedicine-based oral cancer screening in India. Brush biopsy, a minimally invasive sampling technique was combined with a simplified staining protocol and a tablet-based mobile microscope to facilitate local collection of digital images and remote evaluation of the images by clinicians. The tablet-based mobile microscope (CellScope device) combines an iPad Mini with collection optics, LED illumination and Bluetooth-controlled motors to scan a slide specimen and capture high-resolution images of stained brush biopsy samples. Researchers at the Mazumdar Shaw Medical Foundation (MSMF) in Bangalore, India used the instrument to collect and send randomly selected images of each slide for telepathology review. Evaluation of the concordance between gold standard histology, conventional microscopy cytology, and remote pathologist review of the images was performed as part of a pilot study of mobile microscopy as a screening tool for oral cancer. Results indicated that the instrument successfully collected images of sufficient quality to enable remote diagnoses that show concordance with existing techniques. Further studies will evaluate the effectiveness of oral cancer screening with mobile microscopy by minimally trained technicians in low-resource settings.

Mobile health application for remote oral cancer surveillance.

BACKGROUND: To determine the effectiveness of a mobile phone-based remote oral cancer surveillance program (Oncogrid) connecting primary care dental practitioners and frontline health care workers (FHW) with oral cancer specialists. METHODS: The study population (N = 3,440) included a targeted cohort (n = 2,000) and an opportunistic cohort (n = 1,440) screened by FHW and dental professionals, respectively. The authors compared the screening efficacy in both groups, with specialist diagnosis considered the reference standard. The outcomes measured were lesion detection and capture of interpretable images of the oral cavity. RESULTS: In the targeted cohort, among 51 of 81 (61%) interpretable images, 23 of 51 (45%) of the lesions were confirmed by specialists, while the opportunistic cohort showed 100% concordance with the specialists (106 of 106). Sixty-two of 129 (48%) of the recommended patients underwent biopsy; 1 of 23 (4%) were in the targeted cohort, and 61 of 106 (57%) were in the opportunistic cohort. Ninety percent of the lesions were confirmed to be malignant or potentially malignant. CONCLUSIONS: The mobile health-based approach adopted in this study aided remote early detection of oral cancer by primary care dental practitioners in a resource-constrained setting. Further optimization of this program is required to adopt the system for FHW. Evaluation of its efficacy in a larger population is also warranted. PRACTICAL IMPLICATIONS: The increased efficiency of early detection by dentists, when assisted by a remote mobile health-based approach, is a step toward a more effective oral cancer screening program.