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Review of "Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning"

Reviewers: Karla Lee, Tim Spector (King's College) | 📗📗📗📗◻️

by Karla Lee and Tim Spector

Published onOct 07, 2021

This Pub is a Review of

Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning

by Cody T. Mowery, Alexander Marson, Yun S. Song, and Chun Jimmie Ye

Show Description
dx.doi.org

AbstractMitigating transmission of SARS-CoV-2 has been complicated by the inaccessibility and, in some cases, inadequacy of testing options to detect present or past infection. Immunochromatographic lateral flow assays (LFAs) are a cheap and scalable modality for tracking viral transmission by testing for serological immunity, though systematic evaluations have revealed the low performance of some SARS-CoV-2 LFAs. Here, we re-analyzed existing data to present a proof-of-principle machine learning framework that may be used to inform the pairing of LFAs to achieve superior classification performance while enabling tunable False Positive Rates optimized for the estimated seroprevalence of the population being tested.

To read the original manuscript, click the link above.

Summary of Reviews: This preprint assesses a machine learning framework for pairing Immunochromatographic lateral flow assays to increase classification performance. The reviewers deem the findings as novel; however, empathize the limitation of the small sample size.

Reviewer 1 (Karla Lee, Tim Spector) | 📗📗📗📗◻️

RR:C19 Strength of Evidence Scale Key

📕 ◻️◻️◻️◻️ = Misleading

📙📙 ◻️◻️◻️ = Not Informative

📒📒📒 ◻️◻️ = Potentially Informative

📗📗📗📗◻️ = Reliable

📘📘📘📘📘 = Strong

To read the reviews, click the links below.

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A Supplement to this Pub

Review 1: "Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning"

Review 1: "Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning"

by Karla Lee and Tim Spector

Show Description
Published on Oct 07, 2021
rrid.mitpress.mit.edu

This preprint assesses a machine learning framework for pairing Immunochromatographic lateral flow assays to increase classification performance. The reviewers deem the findings as novel; however, empathize the limitation of the small sample size.

License

Creative Commons Attribution 4.0 International License (CC-BY 4.0)

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