This research study evaluates an experimental, point-of-care tool that combines Deep Ultraviolet (DUV) light imaging with Artificial Intelligence (AI) to rapidly screen for oral cancer and pre-cancer (dysplasia).
The main goal is to determine if this automated tool can accurately deliver immediate, same-visit triage results (categorizing cells as either non-dysplasia or dysplasia/cancer) without needing chemical stains, sample transport, or off-site laboratory processing.
The following occurs during a singular clinic visit for participants:
1. Minimally Invasive Brush Test: A clinician gently rotates a soft brush over the mouth tissue to collect surface cells. 2. Stain-Free Imaging \& AI Analysis: The DUV microscope scans the unstained cells in minutes, and the AI analyzes cell features (such as nucleus size and shape) in under 10 seconds. 3. Standard Medical Care: Participants with suspicious spots receive a standard tissue biopsy to ensure a definitive, confirmed diagnosis regardless of the AI result.
This is important given that the five-year survival rates for oral cancer exceed 80% when caught early but drop to under 20% if detected late. By delivering rapid results directly at the clinic, this technology aims to eliminate long waiting periods for lab results, accelerate specialist referrals, and improve access to early screening in community dental and medical clinics.
Suspicious Lesions Cohort: Adults with suspicious oral potentially malignant lesions (OPML), such as leukoplakia, erythroplakia, or speckled lesions), oral precancer (dysplasia), or suspected oral/oropharyngeal squamous cell carcinoma (OSCC) who are scheduled for a diagnostic incisional or excisional tissue punch biopsy.
Healthy Volunteers Cohort: Clinically normal volunteers without suspicious oral mucosal lesions.
Age: Individuals under 18 years of age.
Currently undergoing treatment for malignancy
Unwilling or unable to provide informed consent
Single group
Deep UV-M and AI Cytology Triage
DeviceNon-invasive oral brush cytology harvests full-thickness epithelial cells from suspicious lesions. Cells are smeared onto UV glass slides and fixed in 95% ethanol. Unstained slides are scanned on a point-of-care Deep UV Microscope (DUV-M). An adapted AI model performs per-cell instance segmentation, extracts nuclear morphometric features, and delivers a binary triage result (non-dysplasia vs. dysplasia/cancer).
Triage Classification
The primary endpoint is the accurate binary triage classification (distinguishing non-dysplasia from dysplasia/cancer) performed by the AI DUV-M platform, compared against the reference standard of histopathology.
Three-Class Diagnostic Discrimination
While the primary endpoint focuses on binary triage (non-dysplasia vs. dysplasia/cancer), the secondary analysis evaluates the AI framework's ability to discriminate samples across three distinct diagnostic categories: non-dysplasia, dysplasia, and invasive cancer.
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