Anomaly identification during polymerase chain reaction for detecting SARS-cov-2 using artificial intelligence trained from simulated data
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Fecha
2021
Autores
Villarreal-González, Reynaldo
Acosta-Hoyos, Antonio J.
Garzón-Ochoa, Jaime A.
Galán-Freyle, Nataly J.
Amar-Sepúlveda, Paola
Pacheco-Londoño, Leonardo C.
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MDPI
Facultad de Ingenierías
Facultad de Ingenierías
Resumen
Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute
Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby
meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for
analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed
to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts.
Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no,
and abnormal amplifications. Machine learning (ML) models were generated and tested using small
amounts of data from each class. The best model was used for classifying the big data obtained by
the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2,
and the model was retrained and implemented in a software that correlated patient data with test and
AI diagnoses. The best strategy for AI included a binary classification model, which was generated
from simulated data, where data analyzed by the first model were classified as either positive or
negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated
using the second model. In the first model, the data required preanalysis through a combination of
prepossessing. The early amplification class was eliminated from the models because the numbers of
cases in big data was negligible. ML models can be created from simulated data using minimum
available information. During analysis, changes or variations can be incorporated by generating
simulated data, avoiding the incorporation of large amounts of experimental data encompassing all
possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests
because it enables rapid diagnosis and reduces false positives. Our method can also be used for other
types of molecular analyses.
Descripción
Palabras clave
SARS-CoV-2, Artificial intelligence, Polymerase chain reaction, COVID-19, Simulated data
Citación
Villarreal-González, R.; Acosta- Hoyos, A.J.; Garzon-Ochoa, J.A.; Galán- Freyle, N.J.; Amar-Sepúlveda, P.; Pacheco- Londoño, L.C. Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data. Molecules 2021, 26, 20. https://dx.doi.org/10.3390/ molecules26010020
