The value of artificial intelligence and imaging diagnosis in the fight against COVID-19

Abstract

The outbreak of the new type of coronavirus pneumonia (COVID-19) has caused a huge impact on the world. In this case, only by adhering to the prevention and control methods of early diagnosis, early isolation, and early treatment, can the spread of the virus be prevented to the greatest extent. This article uses artificial intelligence-assisted medical imaging diagnosis as the research object, combines artificial intelligence and CT medical imaging diagnosis, introduces an intelligent COVID-19 detection system, and uses it to achieve COVID-19 disease screening and lesion evaluation. CT examination has the advantages of fast speed and high accuracy, which can provide a favorable basis for clinical diagnosis. This article collected 32 lung CT scan images of patients with confirmed COVID-19. Two professional radiologists analyzed the CT images using traditional imaging diagnostic methods and artificial intelligence-assisted imaging diagnostic methods, and the comparison showed the gap between the two methods. According to experiments, CT imaging diagnosis assisted by artificial intelligence only takes 0.744 min on average, which can save a lot of time and cost compared with the average time of 3.623 min for conventional diagnosis. In terms of comprehensive test accuracy, it can be concluded that the combination of artificial intelligence and imaging diagnosis has extremely high application value in COVID-19 diagnosis.

The Added Effect of Artificial Intelligence on Physicians’ Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review

“…We included a total of 38 studies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ] in our systematic review. The QUADAS-2 tool is presented in Figure 1 , and a PRISMA flowchart of the literature search is presented in Figure 2 .…”

“…We divided the studies into two groups: The first group, consisting of 19 studies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ], used an AI-based device as a concurrent reader in an observer test, where the observers were tasked with diagnosing images with assistance from an AI-based device, while not being allowed (blinded) to see their initial diagnosis made without assistance from AI ( Table 1 a). The second group, consisting of 20 studies [ 19 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ] used the AI-based device as a second reader in an un-blinded sequential observer test, thus allowing observers to see and change their original un-assisted diagnosis ( Table 1 b).…”

“…Bai et al [ 13 ], Dorr et al [ 14 ], Kim et al [ 15 ] Liu et al [ 16 ], Yang et al [ 17 ], and Zhang et al [ 18 ] had AI-based algorithms to detect pneumonia findings of different kinds, e.g., Covid-19 pneumonia from either non-Covid-19 pneumonia or non-pneumonia. Bai et al [ 13 ], Yang et al [ 17 ], Dorr et al [ 14 ], and Zhang et al [ 18 ] investigated detection of Covid-19 pneumonia.…”

“…Bai et al [ 13 ], Dorr et al [ 14 ], Kim et al [ 15 ] Liu et al [ 16 ], Yang et al [ 17 ], and Zhang et al [ 18 ] had AI-based algorithms to detect pneumonia findings of different kinds, e.g., Covid-19 pneumonia from either non-Covid-19 pneumonia or non-pneumonia. Bai et al [ 13 ], Yang et al [ 17 ], Dorr et al [ 14 ], and Zhang et al [ 18 ] investigated detection of Covid-19 pneumonia. Bai et al [ 13 ], Dorr et al [ 14 ], and Yang et al [ 17 ] all had significant improvement in performance measured in sensitivity after being aided by their AI-based devices ( Table 2 a), and Zhang et al [ 18 ] reported shorter reading time per image but there was not any mention of statistical significance ( Table 2 c).…”

The role of artificial intelligence in cancer diagnostics - a review

“…Research on the effectiveness of artificial intelligence shows that when properly used, it achieves similar or better effects than experienced specialists [14]. Additionally, AI requires much less time for data analysis than a human [15], which shortens the diagnostic process.…”