人工智能如何影响胸部CT扫描对COVID-19中肺损伤的评估?

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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, Moscow

Valeria 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, Moscow

Anna 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, Moscow

Anton 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, Moscow

Olesya 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, Moscow

Victor 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

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1. JATS XML
2. 图 2来自各种人工智能服务的原始(对照组)和附加CT系列(测试组和亚组)示例,演示了COVID-19肺部病变分割的 自动图像处理,以及总结肺损伤信息和DICOM SR信息

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3. 图 3 2020年4月8日至2020年12月1日整个期间门诊CT中心根据对照组和试验亚组CT 0-4类别的严重程度,对门诊CT中 心胸部CT的初次CT扫描进行比较。 n=260 594;p<0.0001

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4. 图 42020年11月门诊CT中心根据对照组和试验亚组CT 0-4类别的严重程度,对门诊CT中心胸部CT的初次CT扫描进 行比较。 n=41386;p=0.0010

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版权所有 © Morozov S., Chernina V., Andreychenko A., Vladzymyrskyy A., Mokienko O., Gombolevskiy V., 2021

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