人工智能如何影响胸部CT扫描对COVID-19中肺损伤的评估?
- 作者: Morozov S.1, Chernina V.1, Andreychenko A.1, Vladzymyrskyy A.1, Mokienko O.1, Gombolevskiy V.1
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隶属关系:
- Moscow Center for Diagnostics and Telemedicine
- 期: 卷 2, 编号 1 (2021)
- 页面: 27-38
- 栏目: 原创性科研成果
- URL: https://journals.rcsi.science/DD/article/view/60040
- DOI: https://doi.org/10.17816/DD60040
- ID: 60040
如何引用文章
详细
理由:在大流行期间,计算机断层扫描(CT)是评估与COVID-19相关的肺部变化的主要工具之一。莫斯科的放射学家使用了经过调整的KT0-4量表,根据计算机断层扫描技术,通过视觉评估了一般病情严重程度对COVID-19中肺部改变的放射学征象的性质和严重程度的依赖性。大量的研究中,医生可能会遗漏发现结果并在评估肺损伤量方面犯错误,因此在大流行期间,在门诊医疗中使用AI服务可能很有用。
目的:比较放射科医生形成的CT0-4类别的分布与AI服务处理的结果以及没有AI服务形成的类别的比较。方法:回顾性研究,ClinicalTrials.gov(NCT04489992)。DZM的门诊医疗组织中,分析了从CT0-4类别进行的一次CT扫描的结果,分析时间为:2020年4月8日至2020年1月12日,以及11月(2020年11月1日至2020年1月12日)。根据标准协议在48台计算机断层扫描仪上执行CT,并通过ERIS处理。测试组包括由AI服务处理的CT,对照组为不包含AI的CT。分析包括5种AI服务:RADlogics COVID-19(美国RADLogics),COVID-IRA(俄罗斯的IRA实验室),Care Mentor AI,COVID(俄罗斯的CareMentor AI),第三意见。CT-COVID-19英寸(第三意见,俄罗斯),COVID-MULTIVOX(俄罗斯伽马迈德)。AI服务是随机编码的。
结果:分析了260594例患者的CT扫描结果(m / f%= 44/56,平均年龄-49.5)。测试组包括115,618次CT扫描,对照组-144976。根据特定的AI服务,对于 CT-0类别的不同子组,其设置比对照组少2.3%至18.5%。与未使用AI相比,将CT3-4类别设置为比不使用AI少4.7%至27.6%,并且将CT-4类别与不使用AI设置成从40%至60%(p <0.0001)。
对于11月(从01.11.2020到01.12.2020),分析了41386名患者的CT扫描结果(m / f%= 44/56,平均年龄-53.2岁)。测试组包括28881 CT扫描,对照组-12505。根据特定的AI服务,对于CT-0类别的不同子组,其设置比对照组小1%至2.6%。显示的CT3-4类别比没有使用AI的类别多出0.2%至15.7%; 类别CT-4设置为比不使用AI时少25%(p = 0.001)。
结论:在门诊基础上将AI服务用于主要CT扫描会导致CT-0和CT3-4数量减少,从而影响管理COVID-19患者的策略。
作者简介
Sergey Morozov
Moscow Center for Diagnostics and Telemedicine
Email: morozov@npcmr.ru
ORCID iD: 0000-0001-6545-6170
SPIN 代码: 8542-1720
Scopus 作者 ID: 57200964938
Researcher ID: T-9163-2017
Dr. Sci. (Med.), Professor
俄罗斯联邦, MoscowValeria Chernina
Moscow Center for Diagnostics and Telemedicine
Email: v.chernina@npcmr.ru
ORCID iD: 0000-0002-0302-293X
SPIN 代码: 8896-8051
Scopus 作者 ID: 57210638679
Researcher ID: AAF-1215-2020
MD
俄罗斯联邦, MoscowAnna Andreychenko
Moscow Center for Diagnostics and Telemedicine
Email: a.andreychenko@npcmr.ru
ORCID iD: 0000-0001-6359-0763
SPIN 代码: 6625-4186
Scopus 作者 ID: 42960997200
Researcher ID: E-4930-2017
Cand. Sci. (Phys.-Math.)
俄罗斯联邦, MoscowAnton Vladzymyrskyy
Moscow Center for Diagnostics and Telemedicine
Email: a.vladzimirsky@npcmr.ru
ORCID iD: 0000-0002-2990-7736
SPIN 代码: 3602-7120
Scopus 作者 ID: 8944262100
Researcher ID: D-1447-2017
Dr. Sci. (Med.)
俄罗斯联邦, MoscowOlesya Mokienko
Moscow Center for Diagnostics and Telemedicine
Email: Lesya.md@yandex.ru
ORCID iD: 0000-0002-7826-5135
SPIN 代码: 8088-9921
Scopus 作者 ID: 55155448000
Researcher ID: J-3210-2016
Cand. Sci. (Med.)
俄罗斯联邦, MoscowVictor Gombolevskiy
Moscow Center for Diagnostics and Telemedicine
编辑信件的主要联系方式.
Email: v.gombolevskiy@npcmr.ru
ORCID iD: 0000-0003-1816-1315
SPIN 代码: 6810-3279
Scopus 作者 ID: 57196441765
Researcher ID: J-3389-2017
https://www.scopus.com/authid/detail.uri?authorId=57204359134
Cand. Sci. (Med.), Head of Medical Research Department
俄罗斯联邦, Moscow参考
- Experiment on the use of innovative computer vision technologies for medical image analysis and subsequent applicability in the healthcare system of Moscow [cited 2021 Feb 04]. (In Russ). Available from: https://mosmed.ai
- Morozov SP, Ledikhova NV, Panina EV, et al. Re: Controversy in coronaViral Imaging and Diagnostics (COVID). Clin Radiol. 2020;75(11):871–872. doi: 10.1016/j.crad.2020.07.023
- Chang YC, Yu CJ, Chang SC, et al. Pulmonary sequelae in convalescent patients after severe acute respiratory syndrome: evaluation with thin-section CT. Radiology. 2005;236(3):1067–1075. doi: 10.1148/radiol.2363040958
- Haseli S, Khalili N, Bakhshayeshkaram M, et al. Lobar distribution of COVID-19 pneumonia based on chest computed tomography findings. A retrospective study. Arch Acad Emerg Med. 2020;8(1):e55.
- Inui S, Fujikawa A, Jitsu M, et al. Chest CT findings in cases from the cruise ship “Diamond Princess” with Coronavirus Disease 2019 (COVID-19). Radiol Cardiothorac Imaging. 2020;2(2):e200110. doi: 10.1148/ryct.2020200110
- Prokop M, van Everdingen W, van Rees Vellinga T, et al. CO-RADS: A Categorical CT assessment scheme for patients suspected of having COVID-19-definition and evaluation. Radiology. 2020;296(2):97–104. doi: 10.1148/radiol.2020201473
- Shen C, Yu N, Cai S, et al. Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019. J Pharm Anal. 2020;10(2):123–129. doi: 10.1016/j.jpha.2020.03.004
- Pan F, Ye T, Sun P, et al. Time course of lung changes at chest CT during recovery from Coronavirus Disease 2019 (COVID-19). Radiology. 2020;295(3):715–721. doi: 10.1148/radiol.2020200370
- Revel MP, Parkar AP, Prosch H, et al. COVID-19 patients and the radiology department – advice from the European Society of Radiology (ESR) and the European Society of Thoracic Imaging (ESTI). Eur Radiol. 2020;30(9):4903–4909. doi: 10.1007/s00330-020-06865-y
- Morozov SP, Protsenko DN, Smetanina SV, et al. Radiation diagnosis of coronavirus disease (COVID-19): organization, methodology, interpretation of results: preprint II. Version 2 of 17.04.2020. The series “Best practices of radiation and instrumental diagnostics”. Issue 65. Moscow: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department; 2020. 78 p. (In Russ).
- Sinitsyn VE, Tyurin IE, Mitkov VV. Consensus Guidelines of Russian Society of Radiology (RSR) and Russian Association of Specialists in Ultrasound Diagnostics in Medicine (RASUDM) “Role of Imaging (X-ray, CT and US) in Diagnosis of COVID-19 Pneumonia” (version 2). Journal of Radiology and Nuclear Medicine. 2020;101(2):72–89. (In Russ). doi: 10.20862/0042-4676-2020-101-2-72-89
- Morozov SP, Gombolevskiy VA, Cherninа VY, et al. Prediction of lethal outcomes in COVID-19 cases based on the results chest computed tomography. Tuberculosis and Lung Diseases. 2020;98(6):7–14. (In Russ). doi: 10.21292/2075-1230-2020-98-6-7-14
- Howard J. Cognitive errors and diagnostic mistakes. A case-based guide to critical thinking in medicine. New York: Springer; 2019.
- Morozov SP, Vladzimirsky AV, Klyashtorny VG, et al. Clinical trials of software based on intelligent technologies (radiation diagnostics). The series “Best practices of radiation and instrumental diagnostics”. Issue 57. Moscow: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department; 2019. 51 p. (In Russ).
- Sahiner B, Pezeshk A, Hadjiiski LM, et al. Deep learning in medical imaging and radiation therapy. Med Phys. 2019;46(1):1–36. doi: 10.1002/mp.13264
- Allen BJ, Seltzer SE, Langlotz CP, et al. A road map for translational research on artificial intelligence in medical imaging: from the 2018 national institutes of health/RSNA/ACR/The academy workshop. J Am Coll Radiol. 2019;16(9):1179–1189. doi: 10.1016/j.jacr.2019.04.014
- Angus DC. Randomized clinical trials of artificial intelligence. Jama. 2020;323(11):1043–1045. doi: 10.1001/jama.2020.1039
- Wijnberge M, Geerts BF, Hol L, et al. Effect of a machine learning-derived early warning system for intraoperative hypotension vs standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery: the HYPE randomized clinical trial. Jama. 2020;323(11):1052–1060. doi: 10.1001/jama.2020.0592
- Carlile M, Hurt B, Hsiao A, et al. Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department. J Am Coll Emerg Physicians Open. 2020;1(6):1459–1464. doi: 10.1002/emp2.12297
- Morozov SP, Chernina VYu, Blokhin IA, et al. Chest computed tomography for outcome prediction in laboratory-confirmed COVID-19: A retrospective analysis of 38,051 cases. Digital Diagnostics. 2020;1(1):27−36. (In Russ). doi: 10.17816/DD46791
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