COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

Kim

et al. 2022

Sci Rep

Reducing the time to diagnose COVID-19 helps to manage insufficient isolation-bed resources and adequately accommodate critically ill patients. There is currently no alternative method to real-time reverse transcriptase polymerase chain reaction (RT-PCR), which requires 40 cycles to diagnose COVID-19. We propose a deep learning (DL) model to improve the speed of COVID-19 RT-PCR diagnosis. We developed and tested a DL model using the long short-term memory method with a dataset of fluorescence values measured in each cycle of 5810 RT-PCR tests. Among the DL models developed here, the diagnostic performance of the 21st model showed an area under the receiver operating characteristic (AUROC), sensitivity, and specificity of 84.55%, 93.33%, and 75.72%, respectively. The diagnostic performance of the 24th model showed an AUROC, sensitivity, and specificity of 91.27%, 90.00%, and 92.54%, respectively.

Section: Discussionmentioning

confidence: 99%

The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection

Kim

et al. 2022

Sci Rep

Interdiscip Sci Comput Life Sci

“…This technique then assigns the given data point to the KNN's most familiar class. The number of potential nearest neighbours for KNN chosen were (2,3,5,8,10,12,15,20). XGBoost is an ensemble approach to build a series of trees successively [84].…”

Section: Methodsmentioning

confidence: 99%

“…Numerous applications of Machine Learning (ML) have been utilized in activities such as sanitizing places with drones [6], tracking users using face recognition, drug development, automated robots delivering medicine and food, COVID-19 diagnosis, etc. According to the current literature, ML and hybridised models have been successfully applied in several domains of engineering [7][8][9][10], psychometric analysis [11,12], medical and pharmaceutics [13][14][15], graph theory [16], and social sciences [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning

Chadaga

Chakraborty

Prabhu

et al. 2022

Coronavirus 2 (SARS-CoV-2), often known by the name COVID-19, is a type of acute respiratory syndrome that has had a significant influence on both economy and health infrastructure worldwide. This novel virus is diagnosed utilising a conventional method known as the RT-PCR (Reverse Transcription Polymerase Chain Reaction) test. This approach, however, produces a lot of false-negative and erroneous outcomes. According to recent studies, COVID-19 can also be diagnosed using X-rays, CT scans, blood tests and cough sounds. In this article, we use blood tests and machine learning to predict the diagnosis of this deadly virus. We also present an extensive review of various existing machine-learning applications that diagnose COVID-19 from clinical and laboratory markers. Four different classifiers along with a technique called Synthetic Minority Oversampling Technique (SMOTE) were used for classification. Shapley Additive Explanations (SHAP) method was utilized to calculate the gravity of each feature and it was found that eosinophils, monocytes, leukocytes and platelets were the most critical blood parameters that distinguished COVID-19 infection for our dataset. These classifiers can be utilized in conjunction with RT-PCR tests to improve sensitivity and in emergency situations such as a pandemic outbreak that might happen due to new strains of the virus. The positive results indicate the prospective use of an automated framework that could help clinicians and medical personnel diagnose and screen patients. Graphical abstract

“…There has been mixed levels of success in these fields and there is still a long way to go before AI can be used autonomously for drug discovery. However, at present, Deep Learning and AI allows for the rapid synthesis of heterogeneous data sources to narrow down lists of potential compounds of therapeutic value [ 4 – 8 ]. AI-based approaches have three major components, all subject to their own constraints and limitations.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example

2021

Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.