Mobile oral cancer screening system for low-resource settings

Two-thirds of oral and oropharyngeal squamous cell carcinoma (OSCC) occur in low- and middle-income countries (LMICs), the 5-year survival rate is only 10-40. Enough is already known about the disease and its prevention for action to be taken. The poor survival rate in LMICs is mainly due to late diagnosis. Therefore, it is imperative to detect precursor and malignant lesions early and expeditiously. To meet the need for technologies that enable oral cancer screening and diagnosis in low resource settings (LRS), Light Research Inc (LRI) will develop, validate, and commercialize a low-cost smartphone-based imaging system that provides remote specialist access and triage decision-making guidance tailored to non-specialist use in LRS. To achieve the project goal, LRI will license technologies from University of Arizona (UA), and partner with UA and Mazumdar-Shaw Cancer Center (MSCC) (Bangalore, India) to develop and validate a multimodal mobile oral imaging system for oral cancer detection and diagnosis in LRS. In the past few years, the project team has developed and evaluated a dual-mode (polarized white light imaging [pWLI]) and autofluorescence imaging [AFI])) mobile imaging device that specifically addresses critical barriers in LRS to improve oral cancer screening. To address one of the key hurdles in adopting optical imaging techniques for oral cancer screening in LRS, the team has also developed and evaluated cloud-based and mobile-based deep learning image classification methods for guiding patient triage. Since the key techniques proposed in this mobile imaging system have been successfully evaluated for oral cancer screening with 3,000 high-risk population in LRS, the potential of successfully transitioning it to the low- resource regions for accurate, objective and location-resolved detection of oral cancer is very high. The project objective will be achieved through three Aims: (1) to optimize a mobile intraoral imaging system for LRS, (2) to optimize deep learning-based dual-modality image classification methods, and (3) to validate the clinical usefulness of the mobile oral imaging system for oral cancer screening and triage in LSR. Successful completion of this project will lead to a mobile oral imaging system with deep learning image classification method that delivers urgently-needed capabilities to the end users in LRS. LRI will partner with Jana Care Inc (New Delhi, India) for low-cost production, with Ergo Healthcare (Mumbai, India) for product distribution in south and southeast Asian, and with DentalEZ Group for product distribution in America and Europe. The use of this mobile-based screening approach for early detection and triage of oral cancers will eventually improve oral cancer detection rates, treatment outcomes, and quality of life of patients in LRS.