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In Scandinavian journal of trauma, resuscitation and emergency medicine ; h5-index 32.0

BACKGROUND : Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments.

METHODS : This is a retrospective study carried out between February 22, 2020 and March 16, 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2. Patients under 12 years old and patients in whom the leukocyte formula was not performed in the ED were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol.

RESULTS : Among 199 patients subject to study (median [interquartile range] age 65 [46-78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity.

CONCLUSION : Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.

Langer Thomas, Favarato Martina, Giudici Riccardo, Bassi Gabriele, Garberi Roberta, Villa Fabiana, Gay Hedwige, Zeduri Anna, Bragagnolo Sara, Molteni Alberto, Beretta Andrea, Corradin Matteo, Moreno Mauro, Vismara Chiara, Perno Carlo Federico, Buscema Massimo, Grossi Enzo, Fumagalli Roberto


Artificial intelligence, Critical care, Emergency service, hospital, Pandemics, Severe acute respiratory syndrome coronavirus 2, Supervised machine learning