摘 要
乳腺癌作为女性最常见的恶性肿瘤之一，其流行病学特征和诊疗技术一直是医学研究的热点。乳腺癌的主要风险因素包括生理与遗传因素、激素水平等，而病理类型则分为非浸润性癌和浸润性癌。近年来，随着人工智能技术的飞速发展，其在乳腺癌超声诊疗中的应用逐渐显现出其独特的优势。在乳腺癌超声诊疗中，人工智能通过自动分析与处理乳腺超声图像，实现了乳腺肿瘤的自动检测与分类，极大地提高了诊疗效率。同时，人工智能还能辅助医生进行超声引导下穿刺活检的精准规划，以及预测和评估治疗效果，为乳腺癌的个性化治疗提供了有力支持。人工智能在乳腺癌超声诊疗中也面临着诸多挑战，高质量的数据收集与标注困难，影响了模型的训练效果；技术研发难度高，需要跨学科的知识融合和技术创新；漏诊与误诊风险、伦理与法规问题也是亟待解决的难题。为了应对这些挑战，我们可以采取一系列策略；构建和共享高质量的乳腺超声影像数据集，为模型的训练提供有力保障；加强跨学科合作，整合医学、计算机科学等领域的知识资源；采用多模态融合诊断技术，提高诊断的准确性和可靠性；强化伦理审查和监管，确保人工智能技术的安全、有效和合规。
关键词：人工智能；乳腺癌；超声诊疗
Abstract
Breast cancer is one of the most common malignant tumors in women. its epidemiological characteristics and diagnosis and treatment technology have always been a hot spot in medical research. The main risk factors of breast cancer include physiological and genetic factors, hormone levels, etc. The pathological types are divided into non invasive cancer and invasive cancer. In recent years, with the rapid development of artificial intelligence technology, its application in ultrasound diagnosis and treatment of breast cancer has gradually shown its unique advantages. In the ultrasonic diagnosis and treatment of breast cancer, artificial intelligence realizes the automatic detection and classification of breast tumors by automatically analyzing and processing breast ultrasound images, which greatly improves the diagnosis and treatment efficiency. At the same time, AI can also assist doctors in accurate planning of ultrasound-guided biopsy, and predict and evaluate the treatment effect, providing strong support for personalized treatment of breast cancer. Artificial intelligence also faces many challenges in the ultrasonic diagnosis and treatment of breast cancer. It is difficult to collect and label high-quality data, which affects the training effect of the model; The difficulty of technological research and development is high, requiring interdisciplinary knowledge integration and technological innovation; The risks of missed diagnosis and misdiagnosis, as well as ethical and regulatory issues, are also urgent challenges that need to be addressed. To address these challenges, we can adopt a series of strategies; Building and sharing high-quality breast ultrasound image datasets to provide strong support for model training; Strengthen interdisciplinary cooperation and integrate knowledge resources in fields such as medicine and computer science; Adopting multimodal fusion diagnostic technology to improve the accuracy and reliability of diagnosis; Strengthen ethical review and regulation to ensure the safety, effectiveness, and compliance of artificial intelligence technology.
Keywords：AI Plus Breast cancer; Ultrasound diagnosis ;Treatment
目 录
引 言 1
第一章 乳腺癌流行病学概述 3
1.1 乳腺癌的筛查手段 3
1.2 乳腺癌的主要风险因素 3
1.2.1 生理与遗传因素 3
1.2.2 激素水平 4
1.3 乳腺癌的病理类型 4
1.3.1 非浸润性癌 4
1.3.2 浸润性癌 5
第二章 人工智能在乳腺癌超声诊疗中的应用 6
2.1 乳腺超声图像的自动分析与处理 6
2.2 乳腺肿瘤的自动检测与分类 6
2.3 超声引导下穿刺活检的精准规划 7
2.4 治疗效果的预测与评估 7
第三章 人工智能在乳腺癌超声诊疗中的挑战 9
3.1 数据收集与标注困难 9
3.2 技术研发难度高 9
3.3 漏诊与误诊风险 10
3.4 伦理与法规问题 10
第四章 人工智能在乳腺癌超声诊疗中的应对策略 12
4.1 构建和共享高质量的乳腺超声影像数据集 12
4.2 加强跨学科合作 12
4.3 采用多模态融合诊断技术 12
4.4 强化伦理审查和监管 13
结 论 14
参考文献 15
致 谢 16
