No longer stuck in the past: new advances in artificial intelligence and molecular assays for parasitology screening and diagnosis

医学 寄生虫学 计算生物学 数据科学 医学物理学 病理 生物 计算机科学
作者
Christopher C. Attaway,Blaine A. Mathison,Anisha Misra
出处
期刊:Current Opinion in Infectious Diseases [Ovid Technologies (Wolters Kluwer)]
卷期号:37 (5): 357-366
标识
DOI:10.1097/qco.0000000000001041
摘要

Purpose of review Emerging technologies are revolutionizing parasitology diagnostics and challenging traditional methods reliant on microscopic analysis or serological confirmation, which are known for their limitations in sensitivity and specificity. This article sheds light on the transformative potential of artificial intelligence and molecular assays in the field, promising more accurate and efficient detection methods. Recent findings Artificial intelligence has emerged as a promising tool for blood and stool parasite review, when paired with comprehensive databases and expert oversight result in heightened specificity and sensitivity of diagnoses while also increasing efficiency. Significant strides have been made in nucleic acid testing for multiplex panels for enteric pathogen. Both multiplex and single target panels for Plasmodium , Babesia , filaria, and kinetoplastids have been developed and garnered regulatory approval, notably for blood donor screening in the United States. Additional technologies such as MALDI-TOF, metagenomics, flow cytometry, and CRISPR-Cas are under investigation for their diagnostic utility and are currently in the preliminary stages of research and feasibility assessment. Summary Recent implementation of artificial intelligence and digital microscopy has enabled swift smear screening and diagnosis, although widespread implementation remains limited. Simultaneously, molecular assays – both targeted and multiplex panels are promising and have demonstrated promise in numerous studies with some assays securing regulatory approval recently. Additional technologies are under investigation for their diagnostic utility and are compelling avenues for future proof-of-concept diagnostics.

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