Search

Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient's respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations. [ABSTRACT FROM AUTHOR]

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]

Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI's role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges. [ABSTRACT FROM AUTHOR]

The current coronavirus disease 2019 (COVID-19) outbreak is considered as one of the biggest public health challenges and medical emergencies of the century. A global health emergency demands an urgent development of rapid diagnostic tools and advanced therapeutics for the mitigation of COVID-19. To cope with the current crisis, nanotechnology offers a number of approaches based on abundance and versatile functioning. Despite major developments in early diagnostics and control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is still a need to find effective nanomaterials with low cost, high stability and easy use. Nanozymes are nanomaterials with innate enzyme-like characteristics and exhibit great potential for various biomedical applications such as disease diagnosis and anti-viral agents. Overall the potential and contribution of nanozymes in the fight against SARS-CoV-2 infection i.e., rapid detection, inhibition of the virus at various stages, and effective vaccine development strategies, is not fully explored. This paper discusses the utility and potential of nanozymes from the perspective of COVID-19. Moreover, future research directions and potential applications of nanozymes are highlighted to overcome the challenges related to early diagnosis and therapeutics development for the SARS-CoV-2. We anticipate the current perspective will play an effective role in the existing response to the COVID-19 crisis. [ABSTRACT FROM AUTHOR]