Significance: Screening and early detection of oral potentially malignant lesions (OPMLs) are of great significance in reducing the mortality rates associated with head and neck malignancies. Intra-oral multispectral optical imaging of tissues in conjunction with cloud-based machine learning (CBML) can be used to detect oral precancers at the point-of-care (POC) and guide the clinician to the most malignant site for biopsy. Aim: Develop a bimodal multispectral imaging system (BMIS) combining tissue autofluorescence and diffuse reflectance (DR) for mapping changes in oxygenated hemoglobin (HbO2) absorption in the oral mucosa, quantifying tissue abnormalities, and guiding biopsies. Approach: The hand-held widefield BMIS consisting of LEDs emitting at 405, 545, 575, and 610 nm, 5MPx monochrome camera, and proprietary Windows-based software was developed for image capture, processing, and analytics. The DR image ratio (R610/R545) was compared with pathologic classification to develop a CBML algorithm for real-time assessment of tissue status at the POC. Results: Sensitivity of 97.5% and specificity of 92.5% were achieved for discrimination of OPML from patient normal in 40 sites, whereas 82% sensitivity and 96.6% specificity were obtained for discrimination of abnormal (OPML + SCC) in 89 sites. Site-specific algorithms derived for buccal mucosa (27 sites) showed improved sensitivity and specificity of 96.3% for discrimination of OPML from normal. Conclusions: Assessment of oral cancer risk is possible by mapping of HbO2 absorption in tissues, and the BMIS system developed appears to be suitable for biopsy guidance and early detection of oral cancers.

中文翻译：

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具有基于云的机器学习算法的双模多光谱成像系统，用于口腔潜在恶性病变的实时筛查和检测以及活检指导

意义：口腔潜在恶性病变（OPMLs）的筛查和早期发现对于降低与头颈部恶性肿瘤相关的死亡率具有重要意义。组织的口腔内多光谱光学成像结合基于云的机器学习 (CBML) 可用于在护理点 (POC) 检测口腔癌前病变，并指导临床医生到最恶性的部位进行活检。目的：开发一种结合组织自发荧光和漫反射 (DR) 的双峰多光谱成像系统 (BMIS)，用于绘制口腔黏膜中氧合血红蛋白 (HbO2) 吸收的变化、量化组织异常和指导活检。方法：手持宽场 BMIS 由发射 405、545、575 和 610 nm 的 LED 组成，5MPx 单色相机，并且开发了基于 Windows 的专有软件用于图像捕获、处理和分析。将 DR 图像比率 (R610/R545) 与病理分类进行比较，以开发用于实时评估 POC 组织状态的 CBML 算法。结果：在 40 个位点区分 OPML 与患者正常的敏感性为 97.5%，特异性为 92.5%，而在 89 个位点区分异常（OPML + SCC）的敏感性为 82%，特异性为 96.6%。针对颊黏膜（27 个位点）派生的位点特异性算法表明，区分 OPML 与正常的敏感性和特异性提高了 96.3%。结论：通过绘制组织中的 HbO2 吸收图可以评估口腔癌风险，