Clinical performance of SARS-CoV-2 detection on the cobas Liat using water gargle samples

Veillette, Maxime; Tourangeau, Francine; Fafard, Judith; Dumaresq, Jeannot; Labbé, Annie‐Claude

doi:10.1016/j.jcvp.2022.100108

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…At least theoretically, it is then possible to develop the so-called »Lab-in-a-tube« (i.e., integration of laboratory functions within a chip inserted into the tube) [51] . Although the current applications of these devices are still limited, the remarkable advancements in microfluidics, miniaturized analytics and lab-on-a-chip technology will predictably boost this part of the diagnostic industry in the foreseeable future [52] , especially for rapid and decentralized detection of human pathogens (including SARS-CoV-2) [53] .…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in the pre-analytical phase: State-of-the art and future perspectives

Lippi,

Mattiuzzi,

Favaloro

2024

J Med Biochemistry

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The use of artificial intelligence (AI) has become widespread in many areas of science and medicine, including laboratory medicine. Although it seems obvious that the analytical and post-analytical phases could be the most important fields of application in laboratory medicine, a kaleidoscope of new opportunities has emerged to extend the benefits of AI to many manual labor-intensive activities belonging to the pre-analytical phase, which are inherently characterized by enhanced vulnerability and higher risk of errors. These potential applications involve increasing the appropriateness of test prescription (with computerized physician order entry or demand management tools), improved specimen collection (using active patient recognition, automated specimen labeling, vein recognition and blood collection assistance, along with automated blood drawing), more efficient sample transportation (facilitated by the use of pneumatic transport systems or drones, and monitored with smart blood tubes or data loggers), systematic evaluation of sample quality (by measuring serum indices, fill volume or for detecting sample clotting), as well as error detection and analysis. Therefore, this opinion paper aims to discuss the state-of-the-art and some future possibilities of AI in the preanalytical phase.

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Section: Introductionmentioning

confidence: 99%