# Multimodal Intraoral Imaging System for Oral Cancer Detection and Diagnosis in Low Resource Setting

> **NIH NIH R01** · UNIVERSITY OF ARIZONA · 2022 · $639,824

## Abstract

Oral and oropharyngeal squamous cell carcinoma (OSCC) together rank as the sixth most common cancer
worldwide, accounting for 400,000 new cancer cases each year. Two-thirds of these cancers occur in low- and
middle-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 late
diagnosis and the resultant progression of disease to an advanced stage at diagnosis. Therefore, it is imperative
to 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 low
resource settings (LRS) to identify the suspicious lesions, triage the high-risk subjects and thereby enable
appropriate treatment management and follow up, this project brings together an interdisciplinary team with
complementary expertise in optical imaging, oncology, deep learning, technology translation, and
commercialization. The team will develop, validate, and clinically translate a multimodal intraoral imaging
system for oral cancer detection and diagnosis with better sensitivity and specificity. This work will
address key barriers to adopting optical imaging techniques for oral cancer in LRS by building on the team’s
experience in 1) developing and evaluating dual-mode (polarized white light imaging [pWLI] and
autofluorescence imaging [AFI]) mobile imaging probes; 2) evaluating a low-cost, portable optical coherence
tomography (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-making
guidance. As each of these key techniques has been demonstrated separately for oral cancer imaging in LRS,
the potential of successfully developing a multimodal intraoral imaging system for accurate, objective and
location-resolved diagnosis of oral cancer and transitioning to a new capability to medical professionals in LRS
is very high. To achieve the project objective, the team proposes three Aims: 1) develop a portable, semi-flexible,
and compact multimodal intraoral imaging system; 2) evaluate the clinical feasibility of the prototyped intraoral
imaging system and develop deep learning-based image processing algorithms for early detection, diagnosis,
and mapping of oral dysplastic and malignant lesions; and 3) validate the capability of the prototyped intraoral
imaging system for diagnosing oral dysplasia and malignant lesions.
 Successful completion of this project will lead to the transition of a multimodal intraoral imaging system
and deep learning image classification that leverage the individual strengths of multiple technologies and deliver
new and urgently-needed capabilities to the end users in LRS. This integrated system will 1) detect suspicious
regions with high sensitivity and specificity;...

## Key facts

- **NIH application ID:** 10465103
- **Project number:** 5R01DE030682-02
- **Recipient organization:** UNIVERSITY OF ARIZONA
- **Principal Investigator:** Rongguang Liang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $639,824
- **Award type:** 5
- **Project period:** 2021-08-10 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10465103

## Citation

> US National Institutes of Health, RePORTER application 10465103, Multimodal Intraoral Imaging System for Oral Cancer Detection and Diagnosis in Low Resource Setting (5R01DE030682-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10465103. Licensed CC0.

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