ObjectiveTo determinate the accuracy of computed tomography (CT) imaging assessed by deep neural
networks for predicting the need for mechanical ventilation (MV) in patients
hospitalized with severe acute respiratory syndrome due to coronavirus disease 2019
(COVID-19).Materials and MethodsThis was a retrospective cohort study carried out at two hospitals in Brazil. We
included CT scans from patients who were hospitalized due to severe acute respiratory
syndrome and had COVID-19 confirmed by reverse transcription-polymerase chain reaction
(RT-PCR). The training set consisted of chest CT examinations from 823 patients with
COVID-19, of whom 93 required MV during hospitalization. We developed an artificial
intelligence (AI) model based on convolutional neural networks. The performance of the
AI model was evaluated by calculating its accuracy, sensitivity, specificity, and area
under the receiver operating characteristic (ROC) curve.ResultsFor predicting the need for MV, the AI model had a sensitivity of 0.417 and a
specificity of 0.860. The corresponding area under the ROC curve for the test set was
0.68.ConclusionThe high specificity of our AI model makes it able to reliably predict which patients
will and will not need invasive ventilation. That makes this approach ideal for
identifying high-risk patients and predicting the minimum number of ventilators and
critical care beds that will be required.