A Survey on Artificial Intelligence in Chest Imaging of COVID-19

Chen, Yun; Jiang, Gongfa; Li, Yue; Tang, Yutao; Xu, Yanfang; Ding, Siqi; Xin, Yanqi; Lu, Yao

doi:10.15212/bioi-2020-0015

Cited by 17 publications

(13 citation statements)

References 70 publications

(82 reference statements)

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“…In addition, to make a critical presentation, in the opinion of the authors of this work, of why most of this research leads to unreliable results. This is the main difference of our research with other review studies like [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ] that analyze automatic classification of COVID-19 using CXR images, because none of them address the problems related to the lack of generalization reported in several papers [ 31 ]– [ 34 ].…”

Section: Introductionmentioning

confidence: 81%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 81%

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

et al. 2021

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“…Although LUS results are related to the degree of aeration of the lung’s outer and subpleural layers, they can effectively reflect the condition of lung involvement. Previous study has shown that most COVID-19 pneumonia cases present with peripulmonary and subpleural involvement in the early stage [ 20 ], and this pathological feature is easy to detect by ultrasound. These characteristics of COVID-19 pneumonia provide an ideal application condition for LUS.…”

Section: Discussionmentioning

confidence: 99%

Application of quantitative lung ultrasound instead of CT for monitoring COVID-19 pneumonia in pregnant women: a single-center retrospective study

Deng

Cao

Wang

et al. 2021

BMC Pregnancy Childbirth

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Background Computed tomography (CT) is the preferred imaging technique for the evaluation of COVID-19 pneumonia. However, it is not suitable as a monitoring tool for pregnant women because of the risk of ionizing radiation damage to the fetus as well as the possible infection of others. In this study, we explored the value of bedside lung ultrasound (LUS) as an alternative to CT for the detection and monitoring of lung involvement in pregnant women with COVID-19. Methods Clinical and LUS data of 39 pregnant women with COVID-19 were retrospectively reviewed. All LUS and CT images were analyzed to summarize the findings and calculate LUS scores and CT scores for each patient. LUS findings were compared with CT, and correlation between LUS scores and CT scores was evaluated. Results Among the 39 pregnant women, there were 6 mild-type cases, 29 common-type cases, 4 severe-type cases, and no critical-type cases. The most common LUS findings of COVID-19 pneumonia in pregnant women were various grades of multiple B-lines (84.6%), thickened and irregular pleural lines (71.8%), pleural effusion (61.5%) and small multifocal consolidation limited to the subpleural space (35.9%). The mean LUS score at admission was 0 points in mild-type cases, 10.6 points in common-type cases and 15.3 points in severe-type cases (P < 0.01). The correlation between LUS scores and CT was 0.793. All patients were clinically cured and each underwent an average of three LUS follow-ups during hospitalization. The mean LUS score at discharge was 5.6 points lower than that at admission. The consistency of LUS and chest CT during follow-up was 0.652. Conclusions Quantitative LUS scoring can effectively instead of CT for detecting and monitoring of COVID-19 pneumonia in pregnant women and protect fetuses from the risk of ionizing radiation.

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“…Several researchers have carried out investigations on both CT and X-ray image modalities for COVID and non-COVID classification and reported satisfying accuracy [59] , [60] , [61] . Many transfer learning models were analysed and fine-tuned for extracting features of COVID-19 infected chest X-ray and CT image in [62] , where the VGG-19 model gave out the best accuracy of 99%.…”

Section: Robotics and Ai Technologies In Covid-19 Healthcarementioning

confidence: 99%

Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review

Sarker

Jamal

Ahmed

et al. 2021

Robotics and Autonomous Systems

115

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The outbreak of the COVID-19 pandemic is unarguably the biggest catastrophe of the 21st century, probably the most significant global crisis after the second world war. The rapid spreading capability of the virus has compelled the world population to maintain strict preventive measures. The outrage of the virus has rampaged through the healthcare sector tremendously. This pandemic created a huge demand for necessary healthcare equipment, medicines along with the requirement for advanced robotics and artificial intelligence-based applications. The intelligent robot systems have great potential to render service in diagnosis, risk assessment, monitoring, telehealthcare, disinfection, and several other operations during this pandemic which has helped reduce the workload of the frontline workers remarkably. The long-awaited vaccine discovery of this deadly virus has also been greatly accelerated with AI-empowered tools. In addition to that, many robotics and Robotics Process Automation platforms have substantially facilitated the distribution of the vaccine in many arrangements pertaining to it. These forefront technologies have also aided in giving comfort to the people dealing with less addressed mental health complicacies. This paper investigates the use of robotics and artificial intelligence-based technologies and their applications in healthcare to fight against the COVID-19 pandemic. A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is conducted to accumulate such literature, and an extensive review on 147 selected records is performed.

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A Survey on Artificial Intelligence in Chest Imaging of COVID-19

Cited by 17 publications

References 70 publications

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

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

Application of quantitative lung ultrasound instead of CT for monitoring COVID-19 pneumonia in pregnant women: a single-center retrospective study

Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review

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