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（OSCC）发生在低收入和中等收入人群中。 中低收入国家（LMIC），5年生存率仅为10- 40%。低收入国家的存活率低是由于 诊断和治疗。因此，必须及早和迅速地发现潜在的恶性病变。 为了满足低资源环境（LRS）中口腔癌筛查的需求，我们将开发和验证一种低- 基于移动的电话的成像设备，由计算机视觉和深度学习图像分类提供支持 引导病人分类的算法我们是一个多机构的团队，包括光学成像和 亚利桑那大学纪念斯隆分校的机器学习工程师和口腔/头颈肿瘤学家 凯特林癌症中心和塔塔纪念医院（TMH，孟买，作为LMIC设置）。初步 研究中，我们的团队已经开发并测试了硬件：双模偏振白色光成像（pWLI） 和自体荧光成像（AFI）移动终端。非专业现场医护人员读取图像（低） 灵敏度为60%。此外，一个初步的深度学习分类算法，在云上实现， 基于服务器的计算机，证明了提高79%的敏感性和82%的特异性。我们的建议是 解决关键的剩余障碍-提高非专业现场医疗保健工作者的阅读技能-在当地 在没有互联网和云连接的中低收入国家的LRS中。我们将开发和验证所需的 软件：机器学习（深度学习）图像分类算法在一个移动的手机上，指导现场 医护人员将口腔病变分为良性（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，以 为筛选提供高准确性和一致性。新颖的方面将出现在（i）深度学习方法中 对于双模式图像对比度：正常特征颜色和纹理的pWLI对比度（增加特异性） 和与恶性肿瘤相关的AFI对比度（增加灵敏度），以及（ii）设计用于 在移动的设备上使用，通过教师学生学习为基础的知识蒸馏技术 创新将是第一次在人类测试的敏感性和特异性的改善相对于纯粹的 目视判读，供LRS中的非专业现场医护人员常规使用。在R21项目中， 开发基于移动的深度学习的口腔病变筛查和患者分诊算法，并演示 在癌症护理环境中的可行性（TMH在孟买的主要医院）。在R33项目中，我们将优化 算法，测试和验证在一个大型研究领域设置在TMH的区域诊所在瓦拉纳西。成功 该项目的完成将为LRS的现场医疗工作者提供急需的能力， 口腔潜在恶性病变的检测和分诊，提高早期口腔癌检测率， 及时转诊至专科医生，改善治疗结果，提高中低收入国家患者的生活质量。

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.