诊断学理论与实践

诊断学理论与实践 >

2022 , Vol. 21 >Issue 04: 541 - 546

DOI: https://doi.org/10.16150/j.1671-2870.2022.04.022

综述

机器学习算法在辅助超声诊断附件肿块良恶性中的应用研究进展

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  • 上海交通大学医学院附属瑞金医院妇产科,上海 200025

收稿日期: 2022-02-20

  网络出版日期: 2022-11-07

基金资助

国家自然科学基金面上项目(82172601);上海市科委医学创新项目(20Y11914000)

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Research progress on the application of machine learning in assisted ultrasound diagnosis of adnexal masses

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  • Department of Obstetrics and Gynecology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China

Received date: 2022-02-20

  Online published: 2022-11-07

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摘要

卵巢癌是主要的附件恶性肿瘤,是全球女性癌症相关死亡的第二大常见原因,约75%的患者被诊断时已属晚期,5年生存率低于45%。因此,对附件肿块进行准确的非侵入性良恶性鉴别,对于患者的预后及生存质量至关重要。近年来,人工智能领域进展迅速,机器学习作为人工智能领域的一个分支,具有从大量复杂数据中进行高效学习的能力,逻辑回归、人工神经网络、支持向量机、深度卷积神经网络等算法已被应用于辅助超声鉴别附件肿块良恶性诊断中,并具有良好的诊断效能。本文将对机器学习算法在辅助超声鉴别附件肿块良恶性中应用价值的研究进展进行综述。

关键词： 附件肿块; 卵巢恶性肿瘤; 超声评分体系; 诊断模型; 机器学习

本文引用格式

何新, 陈慧, 冯炜炜 . 机器学习算法在辅助超声诊断附件肿块良恶性中的应用研究进展[J]. 诊断学理论与实践, 2022 , 21(04) : 541 -546 . DOI: 10.16150/j.1671-2870.2022.04.022

Abstract

Ovarian cancer is the second most common cause of gynecologic cancer death in women around the world. Around 75% of patients present with stage Ⅲ/Ⅳ disease at diagnosis, with five-year survival rates below 45%. Ovarian cancer is the main adnexal malignant mass. Thus, accurate non-invasive risk stratification of adnexal masses is essential for optimal management and outcomes. In recent years, the field of artificial intelligence is developing rapidly. As a branch of artificial intelligence, machine learning could learn efficiently from complex and large amounts of data, which has infinite potential to differentiate benign and malignant adnexal masses. Logistic regression(LR), artificial neural network (ANN), support vector machine (SVM), deep learning convolution neural networks (DCNN) have been widely applied in this field, and achieved good diagnostic performance. This paper will review the history and progress of machine learning in ultrasound diagnosis of benign and malignant adnexal masses.

Key words： Adnexal mass; Ovarian malignant tumor; Ultrasound scoring system; Diagnostic model; Machine learning

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