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目前在利用人工智能通过胸部X光成像识别新冠肺炎方面存在的局限性。

Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging.

作者信息

López-Cabrera José Daniel, Orozco-Morales Rubén, Portal-Diaz Jorge Armando, Lovelle-Enríquez Orlando, Pérez-Díaz Marlén

机构信息

Centro de Investigaciones de la Informática, Facultad de Matemática, Física y Computación, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, Villa Clara Cuba.

Departamento de Control Automático, Facultad de Ingeniería Eléctrica, Universidad Central "Marta Abreu" de Las Villas, Villa Clara, Santa Clara, Cuba.

出版信息

Health Technol (Berl). 2021;11(2):411-424. doi: 10.1007/s12553-021-00520-2. Epub 2021 Feb 5.

Abstract

The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.

摘要

科学界已联合起来减轻当前新冠疫情的影响范围。疾病的早期识别以及对其演变的评估是及时应用医疗方案的首要任务。胸部医学影像的使用为专家提供了有价值的信息。具体而言,胸部X光图像一直是许多应用人工智能技术对该疾病进行自动分类研究的重点。迄今为止在该主题上取得的成果很有前景。然而,这些研究的一些结果存在错误,必须加以纠正才能获得适用于临床的模型。本研究讨论了当前科学文献中在将人工智能技术应用于新冠自动分类方面发现的一些问题。显然,在大多数被审查的作品中应用了不正确的评估方案,这导致对结果的高估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73e9/7864619/3a0bd8ee5213/12553_2021_520_Fig1_HTML.jpg

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