Feasibility and Validation of an Automated AI-Enabled Deep Ultraviolet (DUV) Cytology Platform for Point-of-Care Oral Cancer Triage
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 270
- 试验地点
- 1
- 主要终点
- Triage Classification
研究概览
简要总结
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:
- Minimally Invasive Brush Test: A clinician gently rotates a soft brush over the mouth tissue to collect surface cells.
- 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.
- 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.
详细描述
This research project establishes a new diagnostic paradigm by transforming oral brush cytology from a centralized, laboratory-dependent procedure into an automated, AI-assisted point-of-care (POC) triage system. Traditional brush cytology relies on chemical staining, physical sample shipping, and off-site expert review, which can introduce diagnostic turnaround delays. By combining label-free optical imaging with automated hardware and deep learning, this system aims to deliver immediate, same-visit triage of suspicious oral lesions directly within primary dental and outpatient clinics.
Optical Physics & Optomechanical Engineering: The core imaging system utilizes Deep Ultraviolet (DUV) microscopy operating in the 200-280 nm spectral range. At these wavelengths, cellular nucleic acids and proteins exhibit strong intrinsic light absorption, enabling submicron-resolution, high-contrast visualization of nuclear architecture without chemical dyes or fluorescent stains.
Transformer-Based AI Framework (CellViT): Cellular analysis and classification are powered by an adapted CellViT deep learning framework, which replaces traditional object-detection architectures like YOLOv7.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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.
排除标准
- •Age: Individuals under 18 years of age.
- •Currently undergoing treatment for malignancy
- •Unwilling or unable to provide informed consent
研究组 & 干预措施
AI-Enabled Deep UV Brush Cytology with Biopsy Validation
Participants with suspicious oral lesions undergo a quick, non-invasive oral brush test analyzed in real time by the experimental Deep UV AI platform. Immediately after, participants receive the routine 5 mm tissue biopsy to confirm their diagnosis. The AI scan result is compared directly against the biopsy pathology to measure the AI's diagnostic accuracy.
干预措施: Deep UV-M and AI Cytology Triage (Device)
结局指标
主要结局
Triage Classification
时间窗: From the time of oral brush cytology sampling on Day 1 through completion of the reference biopsy histopathology report, assessed up to 14 days post-procedure.
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.
次要结局
- Three-Class Diagnostic Discrimination(From sample collection on Day 1 through completion of 3-class histopathological grading, assessed up to 14 days post-procedure.)
研究者
Anh Le
Norman Vine Endowed Professor and Department Chair
University of Pennsylvania