Oral and oropharyngeal squamous cell carcinoma (OSCC) together rank as the sixth most common cancerworldwide accounting for 400000 new cancer cases each year. Two-thirds of these cancers occur in low- andmiddle-income countries (LMICs). While the 5-year survival rate in the U.S. is 62% the survival rate is only 10-40% and cure rate around 30% in the developing world. The poor survival rate in LMICs is mainly due to latediagnosis and the resultant progression of disease to an advanced stage at diagnosis. Therefore it is imperativeto diagnose precursor and malignant lesions in LRS early and expeditiously. To meet the need for technologies that enable comprehensive oral cancer screening and diagnosis in lowresource settings (LRS) to identify the suspicious lesions triage the high-risk subjects and thereby enableappropriate treatment management and follow up this project brings together an interdisciplinary team withcomplementary expertise in optical imaging oncology deep learning technology translation andcommercialization. The team will develop validate and clinically translate a multimodal intraoral imagingsystem for oral cancer detection and diagnosis with better sensitivity and specificity. This work willaddress key barriers to adopting optical imaging techniques for oral cancer in LRS by building on the teamsexperience in 1) developing and evaluating dual-mode (polarized white light imaging [pWLI] andautofluorescence imaging [AFI]) mobile imaging probes; 2) evaluating a low-cost portable optical coherencetomography (OCT) system for oral cancer detection and diagnosis in a nodal center setting in India; and 3)developing and evaluating deep learning-based image classification algorithms for clinical decision-makingguidance. As each of these key techniques has been demonstrated separately for oral cancer imaging in LRSthe potential of successfully developing a multimodal intraoral imaging system for accurate objective andlocation-resolved diagnosis of oral cancer and transitioning to a new capability to medical professionals in LRSis very high. To achieve the project objective the team proposes three Aims: 1) develop a portable semi-flexibleand compact multimodal intraoral imaging system; 2) evaluate the clinical feasibility of the prototyped intraoralimaging system and develop deep learning-based image processing algorithms for early detection diagnosisand mapping of oral dysplastic and malignant lesions; and 3) validate the capability of the prototyped intraoralimaging system for diagnosing oral dysplasia and malignant lesions. Successful completion of this project will lead to the transition of a multimodal intraoral imaging systemand deep learning image classification that leverage the individual strengths of multiple technologies and delivernew and urgently-needed capabilities to the end users in LRS. This integrated system will 1) detect suspiciousregions with high sensitivity and specificity; 2) triage the high-risk subjects; and 3) guide the selection of biopsysites and map lesion heterogeneity to improve treatment planning and intra-operative guidance.