Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

Asada, Ken; Komatsu, Masaaki; Shimoyama, Ryo; Takasawa, Ken; Shinkai, Norio; Sakai, Akira; Bolatkan, Amina; Yamada, Masayoshi; Takahashi, Satoshi; Machino, Hidenori; Kobayashi, Kazuma; Kaneko, Syuzo; Hamamoto, Ryuji

doi:10.3390/jpm11090886

Cited by 21 publications

(13 citation statements)

References 148 publications

(171 reference statements)

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“…In addition, ML technology plays a central role in the current development of AI. Its importance in medical AI is widely recognized, and many AI-equipped medical devices are widely used in actual clinical settings [ 24 , 132 ]. In fact, single-cell analysis is also expected to contribute to the realization of precision medicine by revealing gene expression profiles that lead to the prediction of patient prognosis and stratification of molecular pathologies; clinical applications of single-cell analysis are expected to progress steadily in the future through the effective use of ML technology.…”

Section: Discussionmentioning

confidence: 99%

Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research

et al. 2021

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In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA sequencing (scRNA-seq) have been reported to analyze cancer constituent cells, identify cell groups responsible for therapeutic resistance, and analyze gene signatures of resistant cell groups. However, although single-cell analysis is a powerful tool, various issues have been reported, including batch effects and transcriptional noise due to gene expression variation and mRNA degradation. To overcome these issues, machine learning techniques are currently being introduced for single-cell analysis, and promising results are being reported. In addition, machine learning has also been used in various ways for single-cell analysis, such as single-cell assay of transposase accessible chromatin sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) analysis, and multi-omics analysis; thus, it contributes to a deeper understanding of the characteristics of human diseases, especially cancer, and supports clinical applications. In this review, we present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential.

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Section: Discussionmentioning

confidence: 99%

Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research

et al. 2021

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“…NMF is a powerful tool for the promotion of precision medicine and is expected to be used in various ways in the field of oncology in the future. Currently, clinical applications of AI are advancing, and machine learning technology is recognized as an important tool for advancing medicine [ 21 , 118–123 ]. In particular, the amount of data to be analyzed in the field of medicine, including whole genome analysis data, is becoming increasingly huge, and this trend is expected to become stronger in the future.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

Hamamoto

Takasawa

Machino

et al. 2022

Briefings in Bioinformatics

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The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.

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“…Moreover, some models—e.g., a novel variational-LSTM autoencoder model introduced by Ibrahim et al [ 15 ] based on DL—can be used to forecast the spread of COVID-19 at the global and country levels. Last but not least, AI techniques can also improve the drug or vaccine development process, which could be faster, cheaper, and more effective [ 16 , 17 ].…”

Section: Covid-19: Detection and Intelligent Models Application—a Bri...mentioning

confidence: 99%

Elements and COVID-19: A Comprehensive Overview of Studies on Their Blood/Urinary Levels and Supplementation with an Update on Clinical Trials

Ścibior

Wnuk

2022

Biology

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The current report provides a brief overview of the clinical features, hematological/biochemical abnormalities, biomarkers, and AI-related strategies in COVID-19; presents in a nutshell the pharmacological and non-pharmacological therapeutic options; and concisely summarizes the most important aspects related to sociodemographic and behavioral factors as well as comorbidities having an impact on this disease. It also gives a brief outline of the effect of selected elements on immune response and collects data on the levels of micro-/macro-elements and toxic metals in the blood/urine of SARS-CoV-2 infected patients and on supplementation with minerals in COVID-19 subjects. Moreover, this review provides an overview of clinical trials based on the use of minerals alone or in combination with other agents that can provide effective responses toward SARS-CoV-2 infection. The knowledge compiled in this report lays the groundwork for new therapeutic treatments and further research on biomarkers that should be as informative as possible about the patient’s condition and can provide more reliable information on COVID-19 course and prognosis. The collected results point to the need for clarification of the importance of mineral supplementation in COVID-19 and the relationships of the levels of some minerals with clinical improvement.

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Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

Cited by 21 publications

References 148 publications

Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research

Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

Elements and COVID-19: A Comprehensive Overview of Studies on Their Blood/Urinary Levels and Supplementation with an Update on Clinical Trials

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