甲状腺超声智能诊断的现状及研究进展

张强; 张仑; 王旭东; 王东; 姚晓峰; 周旋; 李祥春

doi:10.3969/j.issn.1000-8179.2021.04.478

甲状腺超声智能诊断的现状及研究进展

doi: 10.3969/j.issn.1000-8179.2021.04.478

张强^{①, ②,},
张仑^①,
王旭东^①,
王东^①,
姚晓峰^①,
周旋^①,
李祥春^{①, ②, ,}

1.
天津医科大学肿瘤医院颌面耳鼻喉肿瘤科，国家肿瘤临床医学研究中心，天津市“肿瘤防治”重点实验室，天津市恶性肿瘤临床医学研究中心，天津医科大学肿瘤医院肿瘤研究所（天津市300060）
2.
天津市肿瘤分子流行病重点实验室（天津市300060）

基金项目:

国家自然科学基金面上项目 82073287

国家自然科学基金青年科学基金项目 31801117

天津医科大学-深睿医疗联合基金项目 2020120024001236

详细信息

作者简介:
张强专业方向为头颈部肿瘤学研究。E-mail：zhangqiang@tjmuch.com

通讯作者:
李祥春 lixiangchun@tjmuch.com

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- 被引次数: 0
出版历程
- 收稿日期: 2020-11-18
- 刊出日期: 2021-02-28

Research status and progress of ultrasound artificial intelligence in the diagnosis of thyroid tumors

Qiang Zhang^{①, ②
,},
Lun Zhang^①,
Xudong Wang^①,
Dong Wang^①,
Xiaofeng Yao^①,
Xuan Zhou^①,
Xiangchun Li^{①, ②
, ,}

1.
Department of Maxillofacial and Otorhinolaryngology Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
2.
Tianjin Key Laboratory of Tumor Molecular Epidemiology, Tianjin 300060, China

Funds:

the National Natural Science Foundation of China General Project 82073287

National Natural Science Foundation of China Youth Science Fund Project 31801117

Tianjin Medical University-Shenrui Medical Joint Fund Project 2020120024001236

More Information

Corresponding author: Xiangchun Li; E-mail: lixiangchun@tjmuch.com

摘要

摘要: 近几十年在全球范围内甲状腺癌发病率增长迅速。超声检查是甲状腺结节诊断的首选检查手段。作为一项经济、便利且易于推广的检查项目，其对影像学医师的要求较高，需要有丰富的经验。超声检查对甲状腺癌的诊断标准也在不断完善，统一化标准的推广、普及和成熟利用在实施过程中需要大量的人力和财力，目前较难实现。近些年中国医疗卫生资源需求巨大，医疗资源分布欠均衡，临床诊疗中需要全面评估甲状腺结节的恶性风险和颈部淋巴结的性质以进行临床决策，诊断工作繁重复杂。人工智能领域的深度学习算法飞速发展，其在医学图像诊断领域展示出强大的性能，大数据结合深度学习可有效地解决临床诊疗中的问题，深度学习在医疗图像诊断领域、甲状腺结节超声诊断方向显示出较大的优势和应用前景。利用深度学习算法分析超声图像构建超声自动诊断系统，可辅助甲状腺肿瘤超声诊断，简化超声医生的工作流程，有助于提高临床实践效率。本文对近年来深度学习在甲状腺肿瘤及颈部淋巴结超声诊断领域的研究现状和研究进展予以概述。
- 深度学习 /
- 甲状腺肿瘤 /
- 人工智能 /
- 超声
Abstract: In recent decades, the incidence of thyroid cancer has rapidly increased worldwide. Ultrasonography is the first choice of imaging modality for the diagnosis of thyroid nodules because of its low cost and easy availability. Conducting the examination is relatively easy, however, it requires physicians with a high level of expertise and rich experience in delivering imaging services. Ultrasonography is essential for the constant improvement of the diagnostic criteria of thyroid cancer. However, the promotion, popularization, and appropriate use of unified standards require a large amount of manpower and financial resources, thereby making the implementation process quite challenging. A comprehensive evaluation of the malignancy risk of thyroid nodules and the nature of cervical lymph nodes is essential for clinical decision-making, but this evaluation is arduous and complicated. Moreover, the demand for medical and health care resources in China is enormous, and the distribution of medical resources is inequitable. The rapid development of deep learning algorithms in the field of artificial intelligence has demonstrated their powerful performance in the field of medical image diagnosis. Big data combined with deep learning can effectively solve the current problems in clinical diagnosis and treatment. Deep learning has great advantages and application prospects in the field of medical image diagnosis and ultrasound diagnosis of thyroid nodules. The use of deep learning methods to analyze ultrasound images to construct an ultrasound automatic diagnostic system facilitates the ultrasound diagnosis of thyroid tumors and simplifies the work process of ultrasound doctors. Furthermore, it can help improve clinical practice efficiency. This review summarizes the research status and progress of deep learning in the field of ultrasound diagnosis of thyroid tumors and cervical lymph nodes in recent years.
- deep learning /
- thyroid tumor /
- artificial intelligence /
- ultrasonography

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参考文献(42)

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