• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Computer-aided diagnostic system for thyroid nodule sonographic evaluation outperforms the specificity of less experienced examiners.计算机辅助诊断系统在甲状腺结节超声评估中的表现优于经验较少的检查者的特异性。
J Ultrasound. 2020 Jun;23(2):169-174. doi: 10.1007/s40477-020-00453-y. Epub 2020 Apr 3.
2
Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators.基于操作人员经验水平的超声甲状腺结节计算机辅助诊断系统:诊断性能和可重复性。
Eur Radiol. 2019 Apr;29(4):1978-1985. doi: 10.1007/s00330-018-5772-9. Epub 2018 Oct 22.
3
A computer-aided diagnosis system for the assessment and characterization of low-to-high suspicion thyroid nodules on ultrasound.用于超声评估和诊断低-高度疑似甲状腺结节的计算机辅助诊断系统。
Radiol Med. 2019 Feb;124(2):118-125. doi: 10.1007/s11547-018-0942-z. Epub 2018 Sep 22.
4
Computer-Aided Diagnostic System for Thyroid Nodules on Ultrasonography: Diagnostic Performance Based on the Thyroid Imaging Reporting and Data System Classification and Dichotomous Outcomes.基于甲状腺影像报告和数据系统分类和二分类结果的超声甲状腺结节计算机辅助诊断系统:诊断性能。
AJNR Am J Neuroradiol. 2021 Mar;42(3):559-565. doi: 10.3174/ajnr.A6922. Epub 2020 Dec 24.
5
Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiologists with Different Diagnostic Experience.基于甲状腺影像报告和数据系统 (TI-RADS) 的超声计算机辅助诊断 (CAD) 对甲状腺良恶性结节的鉴别诊断及不同诊断经验的放射科医生的诊断效能
Med Sci Monit. 2020 Jan 2;26:e918452. doi: 10.12659/MSM.918452.
6
Real-World Performance of Computer-Aided Diagnosis System for Thyroid Nodules Using Ultrasonography.基于超声的计算机辅助诊断系统在甲状腺结节中的真实世界性能。
Ultrasound Med Biol. 2019 Oct;45(10):2672-2678. doi: 10.1016/j.ultrasmedbio.2019.05.032. Epub 2019 Jun 29.
7
A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment.一种使用人工智能的计算机辅助诊断系统,用于超声检查中甲状腺结节的诊断与特征描述:初步临床评估
Thyroid. 2017 Apr;27(4):546-552. doi: 10.1089/thy.2016.0372. Epub 2017 Feb 28.
8
Inter- and Intraobserver Agreement in the Assessment of Thyroid Nodule Ultrasound Features and Classification Systems: A Blinded Multicenter Study.甲状腺结节超声特征和分类系统评估的观察者间和观察者内一致性:一项盲法多中心研究。
Thyroid. 2020 Feb;30(2):237-242. doi: 10.1089/thy.2019.0360.
9
Interrater Reliability of Various Thyroid Imaging Reporting and Data System (TIRADS) Classifications for Differentiating Benign from Malignant Thyroid Nodules.不同甲状腺影像报告和数据系统(TIRADS)分类在鉴别甲状腺良恶性结节方面的评分者间可靠性
Asian Pac J Cancer Prev. 2019 Apr 29;20(4):1283-1288. doi: 10.31557/APJCP.2019.20.4.1283.
10
Diagnostic performance evaluation of different TI-RADS using ultrasound computer-aided diagnosis of thyroid nodules: An experience with adjusted settings.不同 TI-RADS 应用超声计算机辅助诊断甲状腺结节的诊断性能评估:调整设置后的经验。
PLoS One. 2021 Jan 15;16(1):e0245617. doi: 10.1371/journal.pone.0245617. eCollection 2021.

引用本文的文献

1
Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data.基于甲状腺功能数据区分甲状腺恶性结节与良性结节的机器学习模型
BMJ Open. 2025 May 7;15(5):e093466. doi: 10.1136/bmjopen-2024-093466.
2
Accelerated inference for thyroid nodule recognition in ultrasound imaging using FPGA.使用现场可编程门阵列(FPGA)加速超声成像中甲状腺结节识别的推理过程。
Phys Eng Sci Med. 2025 May 7. doi: 10.1007/s13246-025-01548-8.
3
Role of Artificial Intelligence in Thyroid Cancer Diagnosis.人工智能在甲状腺癌诊断中的作用。
J Clin Med. 2025 Apr 2;14(7):2422. doi: 10.3390/jcm14072422.
4
Student ultrasound education, current view and controversies. Role of Artificial Intelligence, Virtual Reality and telemedicine.学生超声教育、当前观点与争议。人工智能、虚拟现实和远程医疗的作用。
Ultrasound J. 2024 Sep 27;16(1):44. doi: 10.1186/s13089-024-00382-5.
5
Thyroid nodules: diagnosis and management.甲状腺结节:诊断与管理。
Nat Rev Endocrinol. 2024 Dec;20(12):715-728. doi: 10.1038/s41574-024-01025-4. Epub 2024 Aug 16.
6
B-mode Ultrasound Characteristics of Thyroid Nodules With High-Benign Probability and Nodules With Risk of Malignancy.高良性概率甲状腺结节与恶性风险结节的B超特征
Cureus. 2023 May 20;15(5):e39281. doi: 10.7759/cureus.39281. eCollection 2023 May.
7
Artificial intelligence in thyroid ultrasound.甲状腺超声中的人工智能
Front Oncol. 2023 May 12;13:1060702. doi: 10.3389/fonc.2023.1060702. eCollection 2023.
8
Investigating the diagnostic efficiency of a computer-aided diagnosis system for thyroid nodules in the context of Hashimoto's thyroiditis.在桥本甲状腺炎背景下研究计算机辅助诊断系统对甲状腺结节的诊断效率。
Front Oncol. 2023 Jan 5;12:941673. doi: 10.3389/fonc.2022.941673. eCollection 2022.
9
Accuracy of Ultrasound Diagnosis of Thyroid Nodules Based on Artificial Intelligence-Assisted Diagnostic Technology: A Systematic Review and Meta-Analysis.基于人工智能辅助诊断技术的甲状腺结节超声诊断准确性:一项系统评价与Meta分析
Int J Endocrinol. 2022 Sep 23;2022:9492056. doi: 10.1155/2022/9492056. eCollection 2022.
10
Usefulness of a medical interview support application for residents: A pilot study.应用于住院医师的医学访谈辅助应用的实用性:一项试点研究。
PLoS One. 2022 Sep 6;17(9):e0274159. doi: 10.1371/journal.pone.0274159. eCollection 2022.

本文引用的文献

1
Effectiveness evaluation of computer-aided diagnosis system for the diagnosis of thyroid nodules on ultrasound: A systematic review and meta-analysis.超声甲状腺结节诊断中计算机辅助诊断系统的有效性评估:一项系统评价和荟萃分析
Medicine (Baltimore). 2019 Aug;98(32):e16379. doi: 10.1097/MD.0000000000016379.
2
Real-World Performance of Computer-Aided Diagnosis System for Thyroid Nodules Using Ultrasonography.基于超声的计算机辅助诊断系统在甲状腺结节中的真实世界性能。
Ultrasound Med Biol. 2019 Oct;45(10):2672-2678. doi: 10.1016/j.ultrasmedbio.2019.05.032. Epub 2019 Jun 29.
3
Prospective Evaluation of Semiquantitative Strain Ratio and Quantitative 2D Ultrasound Shear Wave Elastography (SWE) in Association with TIRADS Classification for Thyroid Nodule Characterization.半定量应变率比值与二维超声剪切波弹性成像(SWE)联合 TIRADS 分类对甲状腺结节特征分析的前瞻性评估。
Ultraschall Med. 2019 Aug;40(4):495-503. doi: 10.1055/a-0853-1821. Epub 2019 May 28.
4
The comparison of accuracy of ultrasonographic features versus ultrasound-guided fine-needle aspiration cytology in diagnosis of malignant thyroid nodules.超声特征与超声引导下细针穿刺细胞学检查在诊断甲状腺恶性结节中的准确性比较。
J Ultrasound. 2019 Sep;22(3):315-321. doi: 10.1007/s40477-019-00377-2. Epub 2019 Apr 10.
5
Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators.基于操作人员经验水平的超声甲状腺结节计算机辅助诊断系统:诊断性能和可重复性。
Eur Radiol. 2019 Apr;29(4):1978-1985. doi: 10.1007/s00330-018-5772-9. Epub 2018 Oct 22.
6
Reducing the Number of Unnecessary Thyroid Biopsies While Improving Diagnostic Accuracy: Toward the "Right" TIRADS.在提高诊断准确性的同时减少不必要的甲状腺活检:走向“正确”的 TIRADS。
J Clin Endocrinol Metab. 2019 Jan 1;104(1):95-102. doi: 10.1210/jc.2018-01674.
7
A computer-aided diagnosis system for the assessment and characterization of low-to-high suspicion thyroid nodules on ultrasound.用于超声评估和诊断低-高度疑似甲状腺结节的计算机辅助诊断系统。
Radiol Med. 2019 Feb;124(2):118-125. doi: 10.1007/s11547-018-0942-z. Epub 2018 Sep 22.
8
Sonographically Estimated Risks of Malignancy for Thyroid Nodules Computed with Five Standard Classification Systems: Changes over Time and Their Relation to Malignancy.超声评估的五种甲状腺结节良恶性分类系统的风险比较:随时间的变化及其与恶性肿瘤的关系。
Thyroid. 2018 Sep;28(9):1190-1197. doi: 10.1089/thy.2018.0178.
9
Sonographic Presentation of Metastases to the Thyroid Gland: A Case Series.甲状腺转移瘤的超声表现:病例系列
J Endocr Soc. 2018 Jun 21;2(8):855-859. doi: 10.1210/js.2018-00124. eCollection 2018 Aug 1.
10
Computer-Aided Diagnosis of Thyroid Nodules via Ultrasonography: Initial Clinical Experience.计算机辅助超声诊断甲状腺结节:初步临床经验。
Korean J Radiol. 2018 Jul-Aug;19(4):665-672. doi: 10.3348/kjr.2018.19.4.665. Epub 2018 Jun 14.

计算机辅助诊断系统在甲状腺结节超声评估中的表现优于经验较少的检查者的特异性。

Computer-aided diagnostic system for thyroid nodule sonographic evaluation outperforms the specificity of less experienced examiners.

机构信息

Department of Radiological, Oncological, and Pathological Sciences, "Sapienza" University of Rome, Rome, Italy.

Department of Translational and Precision Medicine, "Sapienza" University of Rome, Rome, Italy.

出版信息

J Ultrasound. 2020 Jun;23(2):169-174. doi: 10.1007/s40477-020-00453-y. Epub 2020 Apr 3.

DOI:10.1007/s40477-020-00453-y
PMID:32246401
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7242558/
Abstract

PURPOSE

Computer-aided diagnosis (CAD) may improve interobserver agreement in the risk stratification of thyroid nodules. This study aims to evaluate the performance of the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification as estimated by an expert radiologist, a senior resident, a medical student, and a CAD system, as well as the interobserver agreement among them.

METHODS

Between July 2016 and 2018, 107 nodules (size 5-40 mm, 27 malignant) were classified according to the K-TIRADS by an expert radiologist and CAD software. A third-year resident and a medical student with basic imaging training, both blinded to previous findings, retrospectively estimated the K-TIRADS classification. The diagnostic performance was calculated, including sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve.

RESULTS

The CAD system and the expert achieved a sensitivity of 70.37% (95% CI 49.82-86.25%) and 81.48% (61.92-93.7%) and a specificity of 87.50% (78.21-93.84%) and 88.75% (79.72-94.72%), respectively. The specificity of the student was significantly lower (76.25% [65.42-85.05%], p = 0.02).

CONCLUSION

In our opinion, the CAD evaluation of thyroid nodules stratification risk has a potential role in a didactic field and does not play a real and effective role in the clinical field, where not only images but also specialistic medical practice is fundamental to achieve a diagnosis based on family history, genetics, lab tests, and so on. The CAD system may be useful for less experienced operators as its specificity was significantly higher.

摘要

目的

计算机辅助诊断(CAD)可提高甲状腺结节风险分层的观察者间一致性。本研究旨在评估专家放射科医师、高年住院医师、医学生和 CAD 系统对韩国甲状腺影像报告和数据系统(K-TIRADS)分类的评估性能,以及它们之间的观察者间一致性。

方法

在 2016 年 7 月至 2018 年期间,根据 K-TIRADS 对 107 个结节(大小为 5-40mm,27 个恶性)进行分类,由专家放射科医师和 CAD 软件完成。具有基本成像培训的三年级住院医师和医学生,在不知道先前发现的情况下,回顾性地估计 K-TIRADS 分类。计算了诊断性能,包括敏感性、特异性、阳性和阴性预测值以及受试者工作特征曲线下面积。

结果

CAD 系统和专家的敏感性分别为 70.37%(95%CI,49.82%-86.25%)和 81.48%(61.92%-93.7%),特异性分别为 87.50%(78.21%-93.84%)和 88.75%(79.72%-94.72%)。学生的特异性明显较低(76.25%[65.42%-85.05%],p=0.02)。

结论

在我们看来,CAD 对甲状腺结节分层风险的评估在教学领域具有潜在作用,但在临床领域没有实际和有效的作用,因为不仅需要图像,还需要专业医学实践,以基于家族史、遗传学、实验室检查等来做出诊断。CAD 系统对于经验较少的操作人员可能有用,因为其特异性明显更高。