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The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0–45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics. [ABSTRACT FROM AUTHOR]

COVID-19 is a dangerous disease that directly damages human health, with the properties of severely contagious and highly variable. It is endangering the health and safety of people all around the world. Thus, it compels governments to seek rapid detection, diagnosis and treatment, and epidemic forecasting approaches under the consumption of considerable human resources, material, and financial resources, for the purpose of curbing its development. In view of diverse merits, such as flexibility, rapidity, and non-intrusion, artificial intelligence (AI) techniques have unparalleled advantages in the rapid, non-contact auxiliary diagnosis and epidemic prediction of COVID-19. This paper reviews the AI's technical advances and clinical applications in the COVID-19 epidemic, including computer-aided diagnosis and epidemic prediction, especially the pipelines of medical imaging and analytical techniques. The survey aims to comprehensively investigate the application of AI technologies in the fight against the epidemic and attempt to organize related works in a globally understandable way. This survey also summarizes current challenging issues in the diagnosis and prediction of COVID-19 with AI technologies and puts forward some suggestions for future work. [ABSTRACT FROM AUTHOR]

Due to the unavailability of specific vaccines or drugs to treat COVID-19 infection, the world has witnessed a rise in the human mortality rate. Currently, real time RT-PCR technique is widely accepted to detect the presence of the virus, but it is time consuming and has a high rate of eliciting false positives/negatives results. This has opened research avenues to identify substitute strategies to diagnose the infection. Related works in this direction have shown promising results when RT-PCR diagnosis is complemented with Chest imaging results. Finally integrating intelligence and automating diagnostic systems can improve the speed and efficiency of the diagnosis process which is extremely essential in the present scenario. This paper reviews the use of CT scan, Chest X-ray, lung ultrasound images for COVID-19 diagnosis, discusses the automation of chest image analysis using machine learning and deep learning models, elucidates the achievements, challenges, and future directions in this domain. [ABSTRACT FROM AUTHOR]

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. These alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are first partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. Then, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. Our system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high. [ABSTRACT FROM AUTHOR]