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

三分之二的口腔和口咽鳞状细胞癌(OSCCs)发生在中低收入人群中 国家(LMIC)，5年存活率只有10%-40%。LMICs存活率低的原因是 诊断和治疗。因此，及早、迅速地发现潜在的恶性病变是非常必要的。 为了满足低资源环境下口腔癌筛查的需要，我们将开发和验证低资源环境下的口腔癌筛查。 基于计算机视觉和深度学习图像分类的基于成本的手机成像设备 指导患者分诊的算法。我们是一个多机构团队，由光学成像和 亚利桑那大学斯隆纪念分校的机器学习工程师和口腔/头颈部肿瘤学家 凯特琳癌症中心和塔塔纪念医院(TMH，孟买，作为LMIC的设置)。在预赛中 通过研究，我们的团队开发并测试了硬件：双模偏振白光成像(PWLI) 和自体荧光成像(AFI)移动设备。非专业现场医护人员使用(低)阅读图像 敏感度为60%。此外，在云上实现了一个初步的深度学习分类算法- 基于服务器计算机，其灵敏度和特异度分别提高了79%和82%。我们的建议是 解决剩余的关键障碍--提高非专业现场医护人员的阅读技能--当地 在LMIC的LRS中，这些LMIC没有互联网和云连接。我们将开发和验证所需的 软件：机器学习(深度学习)图像分类算法在手机上，指导场 医护人员将口腔病变分成良性(患者可以回家)和可疑(患者 转介给临床医生进行后续护理)。创新将在计算机视觉的设计和集成方面 (图像镶嵌)和深度学习分类算法在基于手机的成像设备上，以 为筛选提供高准确性和一致性。新的方面将体现在(I)深度学习方法上 对于双模图像对比度：正常功能的颜色和纹理的pWLI对比度(增加特异性) 和AFI对比度与恶性肿瘤(提高敏感性)和(Ii)算法的工程化有关 在移动设备上使用，通过师生学习为基础的知识蒸馏技术的临床 创新将是第一次在人类身上测试相对于纯粹的 目视解说，供LRS的非专业现场医护人员日常使用。在R21项目中，我们将 开发了一种基于移动深度学习的口腔病变筛查和患者分流算法，并进行了演示 在癌症护理环境中的可行性(孟买TMH的主要医院)。在R33项目中，我们将优化 在瓦拉纳西TMH地区诊所的现场环境下进行的大型研究中，对算法进行了测试和验证。成功 该项目的完成将为LRS的现场医护人员提供急需的能力 口腔潜在恶性病变的检测和分类，提高口腔癌的早期发现率，允许 及时转介专家，改善治疗结果，改善LMICs患者的生活质量。

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 capabilities to field healthcare workers in LRS, for early detection and triage of oral potentially malignant lesions, improving early oral cancer detection rates, allowing timely referral to specialists, improving treatment outcomes and improving quality of life for patients in LMICs.