The integration of machine learning and CRISPR/Cas technology in the field of COVID-19 diagnostics

Abstract. The coronavirus known as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2), has generated a global health crisis that has attracted a lot of attention. The necessity of tracking and identifying these variants is highlighted by the appearance of several SARS-CoV-2 variations. Polymerase Chain Reaction-based(PCR-based) assays are considered the gold standard for virus detection, but they have many limitations. The Clustered Regularly Interspaced Short Palindromic Repeats(CRISPR-Cas) system has the potential to overcome these limitations, offering rapidity, sensitivity, specificity, and programmability. Machine learning is also a well-used technology that is widely employed in various fields. Both users and developers of the CRISPR/Cas toolkit have profited from the latest advances and adaptations of its techniques in the field of gene editing. This article reviews the application of CRISPR-Cas and machine learning in SARS-CoV-2 detection. The principles of the CRISPR-Cas system are elucidated, and the application of the combination of the two in detection is explored, providing insightful observations into the advancement of techniques for detecting mutations. In addition, their systematic applications, strengths and weaknesses, challenges and prospects are analyzed.