Smart Pooling is an Artificial Intelligence (AI) system that increases the number of COVID-19 samples that can be tested with the same amount of resources.
Massive molecular testing for COVID-19 has become fundamental to moderate the spread of the disease. While pooling samples can enhance the efficiency of testing, current pooling strategies are only useful at very early stages of the epidemic.
We propose to use clinical and sociodemographic information of patients, to train an AI model to predict the probability of a sample being positive for COVID-19. With this information, we propose a novel pooled testing protocol that triples the efficiency in COVID-19 testing when 6% of the population is infected. Our method could still improve efficiency for prevalences up to 50%. Smart Pooling is an innovative application of Machine Learning that augments the efficiency of testing regardless of the prevalence of COVID-19 or the selected pooling strategy.
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