BioMedical Engineering and Imaging Institute (BMEII) at the Icahn School of Medicine at Mount Sinai
Illustration of the modeling framework: Three AI models are used to generate the probability of a patient being COVID-19 (+): the first is based on a chest CT scan, the second on clinical information; and the third on a combination of the chest CT scan and clinical information. For evaluation of chest CT scans, each slice was first ranked by the probability of containing a parenchymal abnormality, as predicted by the convolutional neural network model (slice selection CNN), which is a pre-trained PTB model that has a 99.4% accuracy to select abnormal lung slices from chest CT scans. The top 10 abnormal CT images per patient were put into the second CNN (diagnosis CNN) to predict the likelihood of COVID-19 positivity (P1). Demographic and clinical data (the patient’s age and sex, exposure history, symptoms and laboratory tests) were put into a machine learning model to classify COVID-19 positivity (P2). Features generated by the diagnosis CNN model and the non-imaging clinical information machine learning model were integrated by a multi-layer perceptron network (MLP) to generate the final output of the joint model (P3). PTB, pulmonary tuberculosis; SVM, support vector machine.