• 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

在结肠镜检查中利用计算机辅助进行息肉检测时,对假阳性警报的基准定义。

Benchmarking definitions of false-positive alerts during computer-aided polyp detection in colonoscopy.

机构信息

Division of Gastroenterology and Hepatology, Tufts Medical Center, Boston, Massachusetts, United States.

Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States.

出版信息

Endoscopy. 2021 Sep;53(9):937-940. doi: 10.1055/a-1302-2942. Epub 2021 Jan 18.

DOI:10.1055/a-1302-2942
PMID:33137833
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8386281/
Abstract

BACKGROUND

The occurrence of false-positive alerts is an important outcome measure in computer-aided colon polyp detection (CADe) studies. However, there is no consensus definition of a false positive in clinical trials evaluating CADe in colonoscopy. We aimed to study the diagnostic performance of CADe based on different threshold definitions for false-positive alerts.

METHODS

A previously validated CADe system was applied to screening/surveillance colonoscopy videos. Different thresholds for false-positive alerts were defined based on the time an alert box was continuously traced by the system. Primary outcomes were false-positive results and specificity using different threshold definitions of false positive.

RESULTS

62 colonoscopies were analyzed. CADe specificity and accuracy were 93.2 % and 97.8 %, respectively, for a threshold definition of ≥ 0.5 seconds, 98.6 % and 99.5 % for a threshold definition of ≥ 1 second, and 99.8 % and 99.9 % for a threshold definition of ≥ 2 seconds.

CONCLUSION

Our analysis demonstrated how different threshold definitions of false positive can impact the reported diagnostic performance of CADe for colon polyp detection.

摘要

背景

假阳性警报的发生是计算机辅助结肠息肉检测 (CADe) 研究中的一个重要结果衡量标准。然而,在评估结肠镜检查中 CADe 的临床试验中,对于假阳性并没有共识的定义。我们旨在研究基于不同假阳性警报阈值定义的 CADe 的诊断性能。

方法

应用经过验证的 CADe 系统对筛查/监测结肠镜视频进行分析。根据系统连续跟踪警报框的时间,定义不同的假阳性警报阈值。主要结果是使用不同的假阳性阈值定义的假阳性结果和特异性。

结果

共分析了 62 例结肠镜检查。对于阈值定义为≥0.5 秒,CADe 的特异性和准确性分别为 93.2%和 97.8%;对于阈值定义为≥1 秒,特异性和准确性分别为 98.6%和 99.5%;对于阈值定义为≥2 秒,特异性和准确性分别为 99.8%和 99.9%。

结论

我们的分析表明,不同的假阳性阈值定义如何影响 CADe 对结肠息肉检测的报告诊断性能。

相似文献

1
Benchmarking definitions of false-positive alerts during computer-aided polyp detection in colonoscopy.在结肠镜检查中利用计算机辅助进行息肉检测时,对假阳性警报的基准定义。
Endoscopy. 2021 Sep;53(9):937-940. doi: 10.1055/a-1302-2942. Epub 2021 Jan 18.
2
Polyp detection and false-positive rates by computer-aided analysis of withdrawal-phase videos of colonoscopy of the right-sided colon segment in a randomized controlled trial comparing water exchange and air insufflation.随机对照试验中比较水交换与注气法对结肠镜检查右半结肠段退镜期视频行计算机辅助分析时息肉检出率和假阳性率。
Gastrointest Endosc. 2022 Jun;95(6):1198-1206.e6. doi: 10.1016/j.gie.2021.12.020. Epub 2021 Dec 30.
3
Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.计算机辅助检测辅助结肠镜检查与常规白光结肠镜检查在前瞻性串联研究中的腺瘤检出率较低。
Gastroenterology. 2020 Oct;159(4):1252-1261.e5. doi: 10.1053/j.gastro.2020.06.023. Epub 2020 Jun 17.
4
Comparing the number and relevance of false activations between 2 artificial intelligence computer-aided detection systems: the NOISE study.比较两个人工智能计算机辅助检测系统之间假阳性激活的数量和相关性:噪声研究。
Gastrointest Endosc. 2022 May;95(5):975-981.e1. doi: 10.1016/j.gie.2021.12.031. Epub 2022 Jan 4.
5
Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning.基于深度学习的计算机辅助结肠息肉定位系统的建立与验证。
World J Gastroenterol. 2021 Aug 21;27(31):5232-5246. doi: 10.3748/wjg.v27.i31.5232.
6
Frame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies.全长结肠镜中商用人工智能息肉检测系统的逐帧分析。
Digestion. 2022;103(5):378-385. doi: 10.1159/000525345. Epub 2022 Jun 29.
7
A video based benchmark data set (ENDOTEST) to evaluate computer-aided polyp detection systems.基于视频的基准数据集(ENDOTEST),用于评估计算机辅助息肉检测系统。
Scand J Gastroenterol. 2022 Nov;57(11):1397-1403. doi: 10.1080/00365521.2022.2085059. Epub 2022 Jun 14.
8
Polyp detection algorithm can detect small polyps: Ex vivo reading test compared with endoscopists.息肉检测算法能够检测出小息肉:与内镜医师相比的体外阅片测试。
Dig Endosc. 2021 Jan;33(1):162-169. doi: 10.1111/den.13670. Epub 2020 May 28.
9
Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.深度学习计算机辅助检测系统对结肠镜检查中腺瘤检测的影响(CADe-DB 试验):一项双盲随机研究。
Lancet Gastroenterol Hepatol. 2020 Apr;5(4):343-351. doi: 10.1016/S2468-1253(19)30411-X. Epub 2020 Jan 22.
10
Assessment of the role of false-positive alerts in computer-aided polyp detection for assistance capabilities.评估计算机辅助息肉检测中假阳性警报在辅助能力方面的作用。
J Gastroenterol Hepatol. 2024 Aug;39(8):1623-1635. doi: 10.1111/jgh.16615. Epub 2024 May 14.

引用本文的文献

1
A comparative study benchmarking colon polyp with computer-aided detection (CADe) software.一项使用计算机辅助检测(CADe)软件对结肠息肉进行基准测试的比较研究。
DEN Open. 2025 Jan 18;5(1):e70061. doi: 10.1002/deo2.70061. eCollection 2025 Apr.
2
A prospective comparison of two computer aided detection systems with different false positive rates in colonoscopy.两种结肠镜检查中具有不同假阳性率的计算机辅助检测系统的前瞻性比较。
NPJ Digit Med. 2024 Dec 19;7(1):366. doi: 10.1038/s41746-024-01334-y.
3
Assessing clinical efficacy of polyp detection models using open-access datasets.使用开放获取数据集评估息肉检测模型的临床疗效。
Front Oncol. 2024 Aug 1;14:1422942. doi: 10.3389/fonc.2024.1422942. eCollection 2024.
4
Non-polypoid Colorectal Lesions Detection and False Positive Detection by Artificial Intelligence under Blue Laser Imaging and Linked Color Imaging.基于蓝光成像和联动成像的人工智能检测非息肉样结直肠病变及假阳性检测
J Anus Rectum Colon. 2024 Jul 30;8(3):212-220. doi: 10.23922/jarc.2023-070. eCollection 2024.
5
Performance comparison between two computer-aided detection colonoscopy models by trainees using different false positive thresholds: a cross-sectional study in Thailand.泰国的一项横断面研究:学员使用不同假阳性阈值对两种计算机辅助检测结肠镜检查模型的性能比较
Clin Endosc. 2024 Mar;57(2):217-225. doi: 10.5946/ce.2023.145. Epub 2024 Feb 7.
6
Single Versus Second Observer vs Artificial Intelligence to Increase the ADENOMA Detection Rate of Colonoscopy-A Network Analysis.单镜、双镜与人工智能辅助提高结肠镜腺瘤检出率:网状分析
Dig Dis Sci. 2024 Apr;69(4):1380-1388. doi: 10.1007/s10620-024-08341-9. Epub 2024 Mar 4.
7
The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization.人工智能在结直肠癌筛查中的作用:病变检测与病变特征分析
Cancers (Basel). 2023 Oct 24;15(21):5126. doi: 10.3390/cancers15215126.
8
A Review of the Technology, Training, and Assessment Methods for the First Real-Time AI-Enhanced Medical Device for Endoscopy.首款用于内窥镜检查的实时人工智能增强型医疗设备的技术、培训及评估方法综述
Bioengineering (Basel). 2023 Mar 24;10(4):404. doi: 10.3390/bioengineering10040404.
9
Artificial intelligence empowers the second-observer strategy for colonoscopy: a randomized clinical trial.人工智能助力结肠镜检查的二次观察策略:一项随机临床试验
Gastroenterol Rep (Oxf). 2023 Jan 19;11:goac081. doi: 10.1093/gastro/goac081. eCollection 2023.
10
Development and evaluation of a deep learning model to improve the usability of polyp detection systems during interventions.开发和评估深度学习模型,以提高介入期间息肉检测系统的可用性。
United European Gastroenterol J. 2022 Jun;10(5):477-484. doi: 10.1002/ueg2.12235. Epub 2022 May 5.