Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data

The Impact of Artificial Intelligence on Microbial Diagnosis

Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

Comparative Analysis of In-House RT-qPCR Detection of SARS-CoV-2 for Resource-Constrained Settings

We developed and standardized an efficient and cost-effective in-house RT-PCR method to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We evaluated sensitivity, specificity, and other statistical parameters by different RT-qPCR methods including triplex, duplex, and simplex assays adapted from the initial World Health Organization- (WHO) recommended protocol. This protocol included the identification of the E envelope gene (E gene; specific to the Sarvecovirus genus), RdRp gene of the RNA-dependent RNA polymerase (specific for SARS-CoV-2), and RNase P gene as endogenous control. The detection limit of the E and the RdRp genes were 3.8 copies and 33.8 copies per 1 µL of RNA, respectively, in both triplex and duplex reactions. The sensitivity for the RdRp gene in the triplex and duplex RT-qPCR tests were 98.3% and 83.1%, respectively. We showed a decrease in sensitivity for the RdRp gene by 60% when the E gene acquired Ct values > 31 in the diagnostic tests. This is associated with the specific detection limit of each gene and possible interferences in the protocol. Hence, developing efficient and cost-effective methodologies that can be adapted to various health emergency scenarios is important, especially in developing countries or settings where resources are limited.

Application of artificial intelligence in COVID-19 medical area: a systematic review

BACKGROUND

Coronavirus disease 2019 (COVID-19) has caused a large-scale global epidemic, impacting international politics and the economy. At present, there is no particularly effective medicine and treatment plan. Therefore, it is urgent and significant to find new technologies to diagnose early, isolate early, and treat early. Multimodal data drove artificial intelligence (AI) can potentially be the option. During the COVID-19 Pandemic, AI provided cutting-edge applications in disease, medicine, treatment, and target recognition. This paper reviewed the literature on the intersection of AI and medicine to analyze and compare different AI model applications in the COVID-19 Pandemic, evaluate their effectiveness, show their advantages and differences, and introduce the main models and their characteristics.

METHODS

We searched PubMed, arXiv, medRxiv, and Google Scholar through February 2020 to identify studies on AI applications in the medical areas for the COVID-19 Pandemic.

RESULTS

We summarize the main AI applications in six areas: (I) epidemiology, (II) diagnosis, (III) progression, (IV) treatment, (V) psychological health impact, and (VI) data security. The ongoing development in AI has significantly improved prediction, contact tracing, screening, diagnosis, treatment, medication, and vaccine development for the COVID-19 Pandemic and reducing human intervention in medical practice.

DISCUSSION

This paper provides strong advice for using AI-based auxiliary tools for related applications of human diseases. We also discuss the clinicians' role in the further development of AI. They and AI researchers can integrate AI technology with current clinical processes and information systems into applications. In the future, AI personnel and medical workers will further cooperate closely.