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开发一个培训网络应用程序,以提高胸部 X 光片对 COVID-19 的诊断准确性。

Developing a Training Web Application for Improving the COVID-19 Diagnostic Accuracy on Chest X-ray.

机构信息

Departamento de Radiología, Hospital Universitario "Marqués de Valdecilla", Santander, Spain.

Departamento Morfología y Biología Celular, Universidad de Oviedo, Oviedo, Spain.

出版信息

J Digit Imaging. 2021 Apr;34(2):242-256. doi: 10.1007/s10278-021-00424-7. Epub 2021 Mar 8.

DOI:10.1007/s10278-021-00424-7
PMID:33686526
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7939450/
Abstract

In December 2019, a new coronavirus known as 2019-nCoV emerged in Wuhan, China. The virus has spread globally and the infection was declared pandemic in March 2020. Although most cases of coronavirus disease 2019 (COVID-19) are mild, some of them rapidly develop acute respiratory distress syndrome. In the clinical management, chest X-rays (CXR) are essential, but the evaluation of COVID-19 CXR could be a challenge. In this context, we developed COVID-19 TRAINING, a free Web application for training on the evaluation of COVID-19 CXR. The application included 196 CXR belonging to three categories: non-pathological, pathological compatible with COVID-19, and pathological non-compatible with COVID-19. On the training screen, images were shown to the users and they chose a diagnosis among those three possibilities. At any time, users could finish the training session and be evaluated through the estimation of their diagnostic accuracy values: sensitivity, specificity, predictive values, and global accuracy. Images were hand-labeled by four thoracic radiologists. Average values for sensitivity, specificity, and global accuracy were .72, .64, and .68. Users who achieved better sensitivity registered less specificity (p < .0001) and those with higher specificity decreased their sensitivity (p < .0001). Users who sent more answers achieved better accuracy (p = .0002). The application COVID-19 TRAINING provides a revolutionary tool to learn the necessary skills to evaluate COVID-19 on CXR. Diagnosis training applications could provide a new original manner of evaluation for medical professionals based on their diagnostic accuracy values, and an efficient method to collect valuable data for research purposes.

摘要

2019 年 12 月,一种新型冠状病毒在中国武汉出现,被称为 2019-nCoV。该病毒已在全球范围内传播,2020 年 3 月宣布该感染为大流行。虽然大多数 2019 年冠状病毒病(COVID-19)病例为轻症,但其中一些病例迅速发展为急性呼吸窘迫综合征。在临床管理中,胸部 X 射线(CXR)至关重要,但 COVID-19 CXR 的评估可能具有挑战性。在这种情况下,我们开发了 COVID-19 TRAINING,这是一种用于 COVID-19 CXR 评估培训的免费网络应用程序。该应用程序包括 196 张 CXR,分为三类:非病理性、与 COVID-19 相符的病理性和与 COVID-19 不相符的病理性。在培训屏幕上,向用户显示图像,他们在这三种可能性中选择一种诊断。用户随时可以完成培训课程,并通过评估其诊断准确性值(敏感性、特异性、预测值和总准确性)来进行评估。图像由四位胸部放射科医生手动标记。敏感性、特异性和总准确性的平均值分别为.72、.64 和.68。具有更好敏感性的用户特异性较低(p <.0001),特异性较高的用户敏感性降低(p <.0001)。发送更多答案的用户准确性更高(p =.0002)。应用程序 COVID-19 TRAINING 提供了一种革命性的工具,可学习评估 COVID-19 所需的 CXR 技能。诊断培训应用程序可以根据其诊断准确性值为医疗专业人员提供一种新的原始评估方式,并且是收集有价值研究数据的有效方法。

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本文引用的文献

1
Diagnostic performance between CT and initial real-time RT-PCR for clinically suspected 2019 coronavirus disease (COVID-19) patients outside Wuhan, China.中国武汉以外地区临床疑似 2019 冠状病毒病(COVID-19)患者的 CT 与实时 RT-PCR 检测的诊断性能比较。
Respir Med. 2020 Jul;168:105980. doi: 10.1016/j.rmed.2020.105980. Epub 2020 Apr 21.
2
COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression.意大利的 COVID-19 疫情:用于定量和监测疾病进展的实验性胸部 X 射线评分系统。
Radiol Med. 2020 May;125(5):509-513. doi: 10.1007/s11547-020-01200-3. Epub 2020 May 1.
3
一种基于CT扫描图像的用于COVID-19感染分类的新型加权共识机器学习模型。
Arab J Sci Eng. 2021 Aug 2:1-12. doi: 10.1007/s13369-021-05879-y.
4
The Imaging Informatics Response to a Pandemic.影像信息学对大流行病的应对。
J Digit Imaging. 2021 Apr;34(2):229-230. doi: 10.1007/s10278-021-00445-2.
Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review.
便携式胸部 X 光在冠状病毒病 19(COVID-19)中的应用:影像学综述。
Clin Imaging. 2020 Aug;64:35-42. doi: 10.1016/j.clinimag.2020.04.001. Epub 2020 Apr 8.
4
Coronavirus infections: Epidemiological, clinical and immunological features and hypotheses.冠状病毒感染:流行病学、临床及免疫学特征与假说
Cell Stress. 2020 Mar 2;4(4):66-75. doi: 10.15698/cst2020.04.216.
5
Clinical and computed tomographic (CT) images characteristics in the patients with COVID-19 infection: What should radiologists need to know?新型冠状病毒肺炎(COVID-19)感染患者的临床和计算机断层扫描(CT)图像特征:放射科医生需要了解什么?
J Xray Sci Technol. 2020;28(3):369-381. doi: 10.3233/XST-200670.
6
Fighting COVID-19: Enabling Graduating Students to Start Internship Early at Their Own Medical School.抗击 COVID-19:让医学生提前在本校开始实习。
Ann Intern Med. 2020 Jul 21;173(2):143-144. doi: 10.7326/M20-1262. Epub 2020 Apr 7.
7
Olfactory and gustatory dysfunctions as a clinical presentation of mild-to-moderate forms of the coronavirus disease (COVID-19): a multicenter European study.嗅觉和味觉障碍作为冠状病毒病(COVID-19)轻至中度形式的临床特征:一项多中心欧洲研究。
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8
Insight into 2019 novel coronavirus - An updated interim review and lessons from SARS-CoV and MERS-CoV.对 2019 年新型冠状病毒的洞察——来自 SARS-CoV 和 MERS-CoV 的更新中期综述和经验教训。
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9
Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2: A systematic review and meta-analysis.新型冠状病毒 SARS-CoV-2 感染患者的影像学和临床特征:系统评价和荟萃分析。
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10
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Int J Biol Sci. 2020 Mar 15;16(10):1753-1766. doi: 10.7150/ijbs.45134. eCollection 2020.