Development and Evaluation of an Automated Multimodal Mobile Detection of Oral Cancer Imaging System to Aid in Risk-Based Management of Oral Mucosal Lesions.

Ruchika Mitbander, David Brenes, Jackson B Coole, Alex Kortum, Imran S Vohra, Jennifer Carns, Richard A Schwarz, Ida Varghese, Safia Durab, Sean Anderson, Nancy E Bass, Ashlee D Clayton, Hawraa Badaoui, Loganayaki Anandasivam, Rachel A Giese, Ann M Gillenwater, Nadarajah Vigneswaran, Rebecca Richards-Kortum
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Abstract

Oral cancer is a major global health problem. It is commonly diagnosed at an advanced stage, although often preceded by clinically visible oral mucosal lesions, termed oral potentially malignant disorders, which are associated with an increased risk of oral cancer development. There is an unmet clinical need for effective screening tools to assist front-line healthcare providers to determine which patients should be referred to an oral cancer specialist for evaluation. This study reports the development and evaluation of the mobile detection of oral cancer (mDOC) imaging system and an automated algorithm that generates a referral recommendation from mDOC images. mDOC is a smartphone-based autofluorescence and white light imaging tool that captures images of the oral cavity. Data were collected using mDOC from a total of 332 oral sites in a study of 29 healthy volunteers and 120 patients seeking care for an oral mucosal lesion. A multimodal image classification algorithm was developed to generate a recommendation of "refer" or "do not refer" from mDOC images using expert clinical referral decision as the ground truth label. A referral algorithm was developed using cross-validation methods on 80% of the dataset and then retrained and evaluated on a separate holdout test set. Referral decisions generated in the holdout test set had a sensitivity of 93.9% and a specificity of 79.3% with respect to expert clinical referral decisions. The mDOC system has the potential to be utilized in community physicians' and dentists' offices to help identify patients who need further evaluation by an oral cancer specialist. Prevention Relevance: Our research focuses on improving the early detection of oral precancers/cancers in primary dental care settings with a novel mobile platform that can be used by front-line providers to aid in assessing whether a patient has an oral mucosal condition that requires further follow-up with an oral cancer specialist.

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口腔癌自动多模态移动检测(mDOC)成像系统的开发和评估,以帮助基于风险的口腔黏膜病变管理。
口腔癌是一个重大的全球健康问题。它通常在晚期被诊断出来,尽管通常在临床可见的口腔粘膜病变之前,称为口腔潜在恶性疾病,与口腔癌发展的风险增加有关。临床需要有效的筛查工具来帮助一线医疗保健提供者确定哪些患者应该转介给口腔癌专家进行评估。本研究报告了口腔癌移动检测(mDOC)成像系统的开发和评估,以及从mDOC图像生成转诊推荐的自动算法。mDOC是一种基于智能手机的自身荧光和白光成像工具,可捕获口腔图像。mDOC收集了29名健康志愿者和120名口腔黏膜病变患者的332个口腔部位的数据。开发了一种多模态图像分类算法,使用专家临床转诊决策作为真实值标签,从mDOC图像中生成“推荐”或“不推荐”的推荐。在80%的数据集上使用交叉验证方法开发了一个推荐算法,然后在一个单独的holdout测试集上重新训练和评估。对于专家临床转诊决定,在拒绝测试集中产生的转诊决定的敏感性为93.9%,特异性为79.3%。mDOC系统有潜力在社区医生和牙医办公室使用,以帮助确定需要由口腔癌专家进一步评估的患者。
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