# Mobile phone-based deep learning algorithm for oral lesion screening in low-resource settings

> **NIH NIH R21** · SLOAN-KETTERING INST CAN RESEARCH · 2024 · $146,188

## Abstract

Two-thirds of oral and oropharyngeal squamous cell carcinomas (OSCCs) occur in low- and middle-income
countries (LMICs), with 5-year survival rates of only 10-40%. The poor survival rate in LMICs is due to late
diagnosis and treatment. Thus, it is imperative to detect potentially malignant lesions early and expeditiously.
To meet the need for oral cancer screening in low resource settings (LRS), we will develop and validate a low-
cost mobile phone-based imaging device powered by computer vision and deep learning image classification
algorithms to guide patient triage. We are a multi-institutional team comprising of optical imaging and
machine learning engineers and oral/head-neck oncologists, at the University of Arizona, Memorial Sloan
Kettering Cancer Center and Tata Memorial Hospital (TMH, Mumbai, as the LMIC setting). In preliminary
studies, our team has developed and tested the hardware: a dual-mode polarized white light imaging (pWLI)
and autofluorescence imaging (AFI) mobile device. Non-expert field healthcare workers read images with (low)
sensitivity of 60%. Additionally, a preliminary deep learning classification algorithm, implemented on a cloud-
based server computer, demonstrated improved sensitivity of 79% and specificity of 82%. Our proposal is to
address the key remaining hurdle – improving the reading skills of non-expert field healthcare workers – locally
in LRS in LMICs, which do not have internet and cloud connectivity. We will develop and validate the required
software: machine learning (deep learning) image classification algorithm on a mobile phone, to guide field
healthcare workers in triage of oral lesions into benign (patients can go home) versus suspicious (patients
referred to clinician for follow up care). The innovations will be in design and integration of computer vision
(image mosaicking) and deep learning classification algorithms on a mobile phone-based imaging device, to
provide high accuracy and consistency for screening. Novel aspects will be in (i) the deep learning approach
for dual-mode image contrast: pWLI contrast for color and texture of normal features (increasing specificity)
and AFI contrast associated with malignancy (increasing sensitivity) and in (ii) engineering of the algorithm for
use on mobile devices, via teacher student learning-based knowledge distillation techniques The clinical
innovation will be first-in-humans testing for improvements in sensitivity and specificity relative to that of purely
visual interpretation, for routine use by non-expert field healthcare workers in LRS. In the R21 project, we will
develop a mobile deep learning-based oral lesion screening and patient triage algorithm and demonstrate
feasibility in a cancer care setting (TMH’s main hospital in Mumbai). In the R33 project, we will optimize the
algorithm, test and validate in a large study in a field setting at TMH’s regional clinic in Varanasi. Successful
completion of this project will deliver urgently needed capa...

## Key facts

- **NIH application ID:** 10861713
- **Project number:** 5R21CA274717-02
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Pankaj Chaturvedi
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $146,188
- **Award type:** 5
- **Project period:** 2023-06-07 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10861713

## Citation

> US National Institutes of Health, RePORTER application 10861713, Mobile phone-based deep learning algorithm for oral lesion screening in low-resource settings (5R21CA274717-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10861713. Licensed CC0.

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