Use of Radiomics and Artificial Intelligence to Predict the Hospitalization Length of Older People with COVID-19

Introduction: The aim of the present study is to build machine learning (ML) architectures, exploiting CT radiomics information, and analyze algorithms’ capability to predict over hospitalization stay of patients affected by COVID-19.Methods and Analysis: Original CT lung images of 168 Covid-19 patients underwent two segmentation phases in order to obtain ground glass area of the lung parenchyma. After an isotropic voxel resampling and wavelet and Laplacian of gaussian filtering, 92 intensity and texture radiomics features were extracted. Feature reduction was conducted by applying Last Absolute Shrinkage and Selection Operator (LASSO) to the radiomic features set. We trained three ML classification algorithms, such as Linear Support Vector Machine (LSVM), Medium Neural Network (MNN) and Ensemble Subspace Discriminant (ESD), and validated through 5-fold cross validation technique. Accuracy, sensitivity, specificity, precision, F1-score and area under the receiving operating characteristic were used to evaluate classification performance.Results: SVM classifier shows the highest average accuracy (86.0%), and AUC (0.93). However reliable outcomes are registered when MNN and ESD architecture are used.Conclusions: The study shows that radiomic features can be used to build a machine learning framework for predicting patient hospitalization duration. Although some limitations occur, i.e. small size of the sample included, the study represents a relevant attempt for the application of CT radiomics for robust prognostic modeling to support health professionals and hospital management team to a better understand diseases and identification of effective treatment options.