Smart pooling: AI-powered COVID-19 informative group testing.

Autor: Escobar M; Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, Colombia., Jeanneret G; Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, Colombia., Bravo-Sánchez L; Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, Colombia., Castillo A; Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, Colombia., Gómez C; Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, Colombia.; Department of Computer Science, Johns Hopkins University, Baltimore, USA., Valderrama D; Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, Colombia., Roa M; Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, Colombia., Martínez J; Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, Colombia., Madrid-Wolff J; Laboratory of Applied Photonics Devices, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland., Cepeda M; School of Science, Universidad de los Andes, Bogotá, Colombia., Guevara-Suarez M; Applied Genomics Research Group, Vice Presidency for Research and Creation, Universidad de los Andes, Bogotá, Colombia., Sarmiento OL; School of Medicine, Universidad de los Andes, Bogotá, Colombia., Medaglia AL; Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, Colombia.; Department of Industrial Engineering, Universidad de los Andes, Bogotá, Colombia., Forero-Shelton M; Department of Physics, Universidad de los Andes, Bogotá, Colombia., Velasco M; Department of Mathematics, Universidad de los Andes, Bogotá, Colombia., Pedraza JM; Department of Physics, Universidad de los Andes, Bogotá, Colombia., Laajaj R; School of Economics, Universidad de los Andes, Bogotá, Colombia., Restrepo S; Applied Genomics Research Group, Vice Presidency for Research and Creation, Universidad de los Andes, Bogotá, Colombia., Arbelaez P; Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogotá, Colombia. pa.arbelaez@uniandes.edu.co.; Department of Biomedical Engineering, Universidad de los Andes, Bogotá, Colombia. pa.arbelaez@uniandes.edu.co.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Apr 20; Vol. 12 (1), pp. 6519. Date of Electronic Publication: 2022 Apr 20.
DOI: 10.1038/s41598-022-10128-9
Abstrakt: Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.
(© 2022. The Author(s).)
Databáze: MEDLINE
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