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  • 陈琢,周欣,张健滔,毕新岭.人工智能领域基底细胞癌的诊治研究进展[J].第二军医大学学报,2019,40(5):471-477    [点击复制]
  • CHEN Zhuo,ZHOU Xin,ZHANG Jian-tao,BI Xin-ling.Advances in diagnosis and treatment of basal cell carcinoma with artificial intelligence[J].Acad J Sec Mil Med Univ,2019,40(5):471-477   [点击复制]
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人工智能领域基底细胞癌的诊治研究进展
陈琢1,3,周欣1,张健滔2,毕新岭1*
0
(1. 海军军医大学(第二军医大学)长海医院皮肤科, 上海 200433;
2. 上海大学机电工程与自动化学院, 上海 200444;
3. 上海交通大学医学院附属上海儿童医学中心皮肤科, 上海 200127
*通信作者)
摘要:
基底细胞癌(BCC)是最常见的皮肤肿瘤之一,临床医师可以根据发病部位、皮损外观特点做出初步诊断,还可以通过皮肤镜等无创检查手段进行图像分析与拟诊,最终依据组织病理结合临床信息确诊。近年随着人工智能(AI)技术的发展,利用机器视觉对图像进行自动识别与分析成为可能。计算机辅助诊断系统通过深度学习大量临床、皮肤镜、组织病理图片资源建立人工神经网络,辅助专业医师对疑难病症做出分析判断,有助于提高皮肤肿瘤的早期诊断水平、增强基层医师诊治能力、减轻病理专科医师工作负担。本文现就国内外AI领域对BCC的诊治研究进展作一综述。
关键词:  基底细胞癌  人工智能  病理学  鉴别诊断  临床表现
DOI:10.16781/j.0258-879x.2019.05.0471
投稿时间:2018-10-02修订日期:2019-01-08
基金项目:上海市浦江人才计划项目(18PJD053),中国中西医结合学会皮肤性病学分会澳美基金(2018),海军军事医学专项(2018JS001).
Advances in diagnosis and treatment of basal cell carcinoma with artificial intelligence
CHEN Zhuo1,3,ZHOU Xin1,ZHANG Jian-tao2,BI Xin-ling1*
(1. Department of Dermatology, Changhai Hospital, Naval Medical University(Second Military Medical University), Shanghai 200433, China;
2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China;
3. Department of Dermatology, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
*Corresponding author)
Abstract:
Basal cell carcinoma (BCC) is one of the most common skin tumors. Dermatologists can make a preliminary diagnosis according to the location and characteristic appearance of the lesions, make a probable diagnosis through analyzing the images by dermoscopy and other non-invasive examination methods, and make a final definite diagnosis by combining histopathology with clinical data. In recent years, with the development of artificial intelligence (AI), it is available to use machine vision for automatic image recognition and analysis. Computer-aided diagnosis system can establish artificial neural network by deep learning great clinical, dermoscopic and histopathological picture data to assist professional physicians to make analysis and judgment on difficult diseases. This is helpful for early diagnosis of skin tumor, elevation the diagnosis capability of junior doctors, and workload reduction of pathologists. Here we reviewed the research progresses of diagnosis and treatment of BCC in AI field at home and abroad.
Key words:  basal cell carcinoma  artificial intelligence  pathology  differential diagnosis  clinical manifestation