Classification of oral lesions using deep learning for early detection of oral cancer

使用深度学习对口腔病变进行分类以早期发现口腔癌

• 批准号: MR/S013865/1
• 负责人: Sarah Barman
• 金额: $ 18.72万
• 依托单位: Kingston University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS013865%2F1
• 关键词: Classification; oral; lesions; using; deep

For the majority of cancers, early detection results in better survival. Oral cancer is one of the few cancers that is visible and many of these cancers are preceded by a potentially malignant lesion where medical intervention can prevent the development of cancer. Taken together, oral cancer presents an opportunity for early detection. However, identifying which oral lesion has a propensity to become oral cancer is not straightforward without specialised training and this problem is confounded by the lack of specialists who are trained in this expertise particularly in low- and middle-income countries, where the majority of oral cancers are diagnosed. One innovative approach to overcome this is to develop an artificial intelligence algorithm to classify oral lesions into those that are benign and those that are potentially malignant or are occult cancer so that patients can be triaged accordingly to receive appropriate clinical management. In this project, we propose to work within a multi-disciplinary, international team to collate a library of images from existing and prospective collections that will facilitate the development of an artificial intelligence algorithm that will be tested and validated. The outcome of this project will pave the way for further rigorous testing, development of an App incorporating this automated tool and clinical validation for the early detection of oral cancer. The development of an automated tool for the classification of oral lesions will facilitate the identification of patients most at risk to develop oral cancer so that these individuals can be managed appropriately. This project is particularly impactful in the low- and middle-income countries as the majority of the global burden of oral cancer is found in these countries.

对于大多数癌症来说，早期发现可以提高生存率。口腔癌是少数可见的癌症之一，其中许多癌症之前都有潜在的恶性病变，医学干预可以预防癌症的发展。总之，口腔癌提供了早期发现的机会。然而，如果没有专业培训，确定哪些口腔病变有成为口腔癌的倾向是不容易的，而且由于缺乏受过这方面专业培训的专家，特别是在大多数口腔癌得到诊断的低收入和中等收入国家，这一问题更加复杂。克服这一问题的一种创新方法是开发一种人工智能算法，将口腔病变分为良性和潜在恶性或隐匿性癌症，以便对患者进行相应的分类，接受适当的临床治疗。在这个项目中，我们建议与一个多学科的国际团队合作，从现有和未来的收藏中整理一个图片库，这将促进人工智能算法的开发，并将进行测试和验证。该项目的结果将为进一步严格的测试、开发包含该自动化工具的应用程序以及早期检测口腔癌的临床验证铺平道路。口腔病变自动分类工具的开发将有助于识别最有可能患口腔癌的患者，从而对这些患者进行适当的治疗。该项目在低收入和中等收入国家尤其具有影响力，因为全球口腔癌负担的大部分发生在这些国家。

项目成果

Image collection and annotation platforms to establish a multi-source database of oral lesions.

图像采集和标注平台，建立口腔病变多源数据库。

• DOI: 10.1111/odi.14206
• 发表时间: 2023
• 期刊: Oral diseases
• 影响因子: 3.8
• 作者: Rajendran S

Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer

• DOI: 10.1109/access.2020.3010180
• 发表时间: 2020-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Welikala, Roshan Alex;Remagnino, Paolo;Barman, Sarah Ann
• 通讯作者: Barman, Sarah Ann

Medical Image Understanding and Analysis - 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12-14, 2021, Proceedings

医学图像理解与分析 - 第 25 届年会，MIUA 2021，英国牛津，2021 年 7 月 12-14 日，会议记录

• DOI: 10.1007/978-3-030-80432-9_17
• 发表时间: 2021
• 作者: Bagur A