A Deep Learning System to Diagnose COVID-19 Pneumonia Using Masked Lung CT Images to Avoid AI-generated COVID-19 Diagnoses that Include Data outside the Lungs
A Deep Learning System to Diagnose COVID-19 Pneumonia Using Masked Lung CT Images to Avoid AI-generated COVID-19 Diagnoses that Include Data outside the Lungs
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DOI：
10.14326/abe.11.76
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发表时间：
2022
影响因子：
1
通讯作者：
T. Nagaoka;T. Kozuka;Takahiro Yamada;H. Habe;M. Nemoto;M. Tada;K. Abe;H. Handa;Hisashi Yoshida;Kazunari Ishii;Yuichi Kimura
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文献类型：
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作者：
T. Nagaoka;T. Kozuka;Takahiro Yamada;H. Habe;M. Nemoto;M. Tada;K. Abe;H. Handa;Hisashi Yoshida;Kazunari Ishii;Yuichi Kimura
目的：本研究的目的是开发一种新型的基于人工智能（AI）的系统，使用计算机断层扫描（CT）切片图像诊断冠状病毒疾病（COVID-19）。此前的研究已经证明，如果不关注肺部，人工智能会使用肺部以外的信息诊断COVID-19。包含来自多个设施和CT模型的CT训练数据也可能导致AI使用与COVID-19无关的特征来诊断COVID-19。因此，当前研究的目的是使用单个CT模型评估来自单个设施的肺掩模图像和CT切片图像的组合，并使用AI仅基于与肺部相关的信息来区分COVID-19与其他类型的肺炎。方法：通过使用现有的AI结构将肺部掩模图像叠加在图像特征输出上，可以排除肺部周围以外的图像特征。该模型的结果还与仅提取肺部区域的切片图像结果进行了比较。该系统采用了整体方法。根据CT切片图像，对多个AI的输出进行平均，以区分COVID-19病例与其他类型的肺炎。结果：该系统评估了使用单个CT模型在单个设施中拍摄的132个COVID-19病例扫描和62个非COVID-19病例扫描。初始的灵敏度，特异性和准确性，我们的系统，使用阈值为0.50，分别为95%，53%和81%。将阈值设置为0.84，将灵敏度和特异性分别调整为76%和84%的临床可用值。结论：目前研究开发的系统能够区分COVID-19引起的肺炎和其他类型的肺炎，具有足够的准确性，可用于临床实践。与之前的研究相比，尽管应用了更严格的条件，但这是在没有包含临床无意义区域的图像的情况下实现的。
Objective: The objective of the current study was to develop a novel, artificial intelligence (AI)-based system to diagnose coronavirus disease (COVID-19) using computed tomography (CT) slice images. Prior research has demonstrated that, if not focused on the lungs, AI diagnoses COVID-19 using information outside the lungs. The inclusion of CT training data from multiple facilities and CT models may also cause AI to diagnose COVID-19 with features that are irrelevant to COVID-19. Thus, the objective of the current study was to evaluate a combination of lung mask images and CT slice images from a single facility, using a single CT model, and use AI to differentiate COVID-19 from other types of pneumonia based solely on information related to the lungs. Method: By superimposing lung mask images on image feature output using an existing AI structure, it was possible to exclude image features other than those around the lungs. The results of this model were also compared with the slice image findings from which only the lung region was extracted. The system adopted an ensemble approach. The outputs of multiple AIs were averaged to differentiate COVID-19 cases from other types of pneumonia, based on CT slice images. Results: The system evaluated 132 scans of COVID-19 cases and 62 scans of non-COVID-19 cases taken at the single facility using a single CT model. The initial sensitivity, specificity, and accuracy of our system, using a threshold value of 0.50, was shown to be 95%, 53%, and 81%, respectively. Setting the threshold value to 0.84 adjusted the sensitivity and specificity to clinically usable values of 76% and 84%, respectively. Conclusion: The system developed in the current study was able to differentiate between pneumonia due to COVID-19 and other types of pneumonia with sufficient accuracy for use in clinical practice. This was accomplished without the inclusion of images of clinically meaningless regions and despite the application of more stringent conditions, compared to prior studies.