Abstract

Background:

Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment.

Purpose:

In this study, a deep learning algorithm-based AI model using ResUNet network was developed to evaluate the performance of radiologists with and without AI assistance in distinguishing COVID-19 infected pneumonia patients from other pulmonary infections on CT scans.

Methods:

For model development and validation, a total number of 694 cases with 111,066 CT slides were retrospectively collected as training data and independent test data in the study. Among them, 118 are confirmed COVID-19 infected pneumonia cases and 576 are other pulmonary infections cases (e.g. tuberculosis cases, common pneumonia cases and non-COVID-19 viral pneumonia cases). The cases were divided into training and testing datasets. The independent test was performed by evaluating and comparing the performance of three radiologists with different years of practice experience in distinguishing COVID-19 infected pneumonia cases with and without the AI assistance.

Results:

Our final model achieved an overall test accuracy of 0.914 with an area of the receiver operating characteristic (ROC) curve (AUC) of 0.903 in which the sensitivity and specificity are 0.918 and 0.909, respectively. The deep learning-based model then achieved a comparable performance by improving the radiologists’ performance in distinguish COVOD-19 from other pulmonary infections, yielding better average accuracy and sensitivity, from 0.941 to 0.951 and from 0.895 to 0.942, respectively, when compared to radiologists without using AI assistance.

Conclusion:

A deep learning algorithm-based AI model developed in this study successfully improved radiologists’ performance in distinguishing COVID-19 from other pulmonary infections using chest CT images.

中文翻译：

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使用人工智能协助放射科医生区分 COVID-19 与其他肺部感染

 抽象的

 背景：

准确、快速诊断冠状病毒病（COVID-19）对于及时隔离和治疗至关重要。

 目的：

在这项研究中，开发了一种使用 ResUNet 网络的基于深度学习算法的 AI 模型，以评估放射科医生在有或没有 AI 辅助的情况下在 CT 扫描中区分 COVID-19 感染的肺炎患者与其他肺部感染的表现。

 方法：

为了模型的开发和验证，研究中回顾性收集了总共 694 例病例、111,066 张 CT 切片作为训练数据和独立测试数据。其中，确诊的COVID-19感染肺炎病例118例，其他肺部感染病例（例如肺结核病例、普通肺炎病例和非COVID-19病毒性肺炎病例）576例。这些案例分为训练数据集和测试数据集。这项独立测试是通过评估和比较三名具有不同年限实践经验的放射科医生在有和没有人工智能辅助的情况下区分 COVID-19 感染肺炎病例的表现来进行的。

 结果：

我们的最终模型的总体测试精度为 0.914，受试者工作特征 (ROC) 曲线 (AUC) 面积为 0.903，其中敏感性和特异性分别为 0.918 和 0.909。然后，基于深度学习的模型通过提高放射科医生区分 COVOD-19 与其他肺部感染的表现，实现了可比的性能，与放射科医生相比，平均准确度和灵敏度分别从 0.941 提高到 0.951 和从 0.895 提高到 0.942无需使用人工智能辅助。

 结论：

本研究中开发的基于深度学习算法的 AI 模型成功提高了放射科医生使用胸部 CT 图像区分 COVID-19 与其他肺部感染的表现。