# NIH grant: Advancing FTIR-based Multimodal Imaging with AI for Risk Stratification of Oral Precancer (1R01DE036248-01) — retained record

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[original source ↗](https://reporter.nih.gov/project-details/1R01DE036248-01)

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NIH grant 1R01DE036248-01: Advancing FTIR-based Multimodal Imaging with AI for Risk Stratification of Oral Precancer
Institute: {'code': 'DE', 'abbreviation': 'NIDCR', 'name': 'National Institute of Dental and Craniofacial Research', 'admin_org_id': '12500070', 'admin_funding_url': ''}. PI: Rong  Wang. Award: $527533.
Start: 2026-09-19T00:00:00.
PROJECT SUMMARY/ABSTRACT
Successful treatment and management of oral mucosal lesions depend on definitive, accurate, and timely
diagnosis. Oral squamous cell carcinoma (OSCC) is highly aggressive and often diagnosed at late stages,
leading to poor prognosis. A significant portion of OSCC is preceded by oral potentially malignant disorder
(OPMD), a heterogeneous group of precancerous oral lesions associated with an increased risk of malignant
transformation (MT). However, gold-standard histopathological diagnosis is subjective and lacks predictive
power for assessing malignant transformation risk in individual OPMD patients, creating a critical barrier in oral
precancer management. To address this unmet medical need, we propose an innovative multimodal imaging
system that integrates FTIR imaging and histological imaging to capture both biochemical and morphological
features of tissue. Leveraging advanced machine learning (ML) and deep learning (DL) methods, we will develop
predictive models based on the two complementary imaging techniques for OPMD risk stratification. Our central
hypothesis is that AI-driven integration of biochemical fingerprints from FTIR imaging with morphological features
from histological imaging will yield a more precise model for predicting MT in OPMDs, outperforming traditional
histological assessment or either modality alone. To test this hypothesis, we propose the following specific aims:
1) Develop FTIR imaging-based ML and DL models for OPMD risk stratification; 2) Develop histological imaging-
based DL models for OPMD tissue segmentation and risk stratification; and 3) Develop and validate multimodal
imaging systems for OPMD risk stratification. The proposed system can be easily incorporated into existing
clinical workflows to enhance OPMD diagnosis and management, facilitate early oral cancer detection, and
ultimately reduce the global burden of the disease.
```



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