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.

Role of community health worker in a mobile health program for early detection of oral cancer.

BACKGROUND: The global incidence of oral cancer occurs in low-resource settings. Community-based oral screening is a strategic step toward downstaging oral cancer by early diagnosis. The mobile health (mHealth) program is a technology-based platform, steered with the aim to assess the use of mHealth by community health workers (CHWs) in the identification of oral mucosal lesions. MATERIALS AND METHODS: mHealth is a mobile phone-based oral cancer-screening program in a workplace setting. The participants were screened by two CHWs, followed by an assessment by an oral medicine specialist. A mobile phone-based questionnaire that included the risk assessment was distributed among participants. On specialist recommendation an oral surgeon performed biopsy on participants. The diagnosis by onsite specialist that was confirmed by histopathology was considered as gold standard. All individuals received the standard treatment protocol. A remote oral medicine specialist reviewed the uploaded data in Open Medical Record System. Sensitivity, specificity, positive and negative predictive values were calculated. Inter-rater agreement was analyzed with Cohen's kappa coefficient (κ) test, and the diagnostic ability of CHWs, onsite specialist, and remote specialist was illustrated using receiver operating characteristic curve. RESULTS: CHWs identified oral lesions in 405 (11.8%) individuals; the onsite specialist identified oral lesions in 394 (11.4%) individuals; and the remote specialist diagnosed oral lesions in 444 (13%). The inter-rater agreement between the CHW and the onsite specialist showed almost perfect agreement with the κ score of 0.92, and a substantial agreement between CHW and remote specialist showed a score of 0.62. The sensitivity, specificity, positive and negative predictive values of CHWs in the identification of oral lesion were 84.7, 97.6, 84.8, and 97.7%, respectively. CONCLUSION: The trained CHWs can aid in identifying oral potentially malignant disorders and they can be utilized in oral cancer-screening program mHealth effectively.

Smokeless Tobacco-associated Lesions: A Mobile Health Approach.

AIM: Globally, India accounts for the highest number of oral cancer cases. The survival rates are about 30% lower than those in developing countries. The main reason for these dismal figures is the late presentation of patients. In order to downstage oral cancer in such a scenario, screening and diagnosis at an early stage is warranted. A pragmatic approach is needed for an oral cancer screening program, hence a mobile health (mHealth) approach was used. In this approach, health workers were empowered with mobile phones with decision-based algorithm. Risk stratification of tobacco habit enables us to identify lesions associated with particular habits. MATERIALS AND METHODS: A specific cohort of factory employees who predominantly had pure tobacco chewing habit was chosen to examine the effect of pure tobacco on oral mucosa. One thousand three hundred and fifty-seven subjects were screened in two phases. In the first phase, habits and oral lesions were identified and photographed. The photographs were remotely diagnosed by an oral medicine specialist and those subjects requiring biopsy were recalled for phase II. Cytology and biopsy were performed in phase II. RESULTS: The predominant habit was smokeless tobacco (SLT), in 582 subjects. The most commonly encountered lesion was tobacco pouch keratosis seen in 397 subjects. Biopsy was performed for 71 subjects, most cases showed hyperkeratosis and mild dysplasia. One subject had moderate dysplasia. CONCLUSION: There was minimal alteration of tissues in our study subjects, which can be considered as low-risk. Use of mHealth empowered frontline healthcare workers to identify subjects with lesions and enabled remote diagnosis by specialist in resource-constrained settings. CLINICAL SIGNIFICANCE: Use of mHealth enabled us have an electronic record of subject details. This data shall be used for a planned follow-up of the same cohort after 3 years.