Integration of a Deep Learning-Based Module for the Quantification of Imaging Features into the Filling-in Process of the Radiological Structured Report

The role of Computed Tomography (CT) in the characterization of COVID-19 pneumonia has been widely recognized. The aim of this work is to present the idea of integrating a Deep Learning (DL)-based software, able to automatically quantify qualitative information typically describing COVID-19 lesions on chest CT scans, into a structured report-filling pipeline. Different studies have highlighted the value of introducing the use of structured reports in clinical practice, as a reproducible instrument for diagnosis and follow-up rather than the commonly used free-text radiological report. Structured data are fundamental to helping clinical decision support systems and fostering precision medicine. We developed a Deep Learning based software that segments both the lungs and the lesions associated with COVID-19 pneumonia on chest CT scans and quantifies some indexes describing qualitative characteristics used to assess COVID-19 lesions clinically. Once assessed the robustness of the system by means of a multicenter clinical evaluation made by clinical experts, it can be used for the first stratification of patients, supporting radiologists with a computer-aided quantification, and the derived quantities, immediately intelligible for the clinicians, are suitable to be inserted in a structured report in COVID-19 pneumonia and then exploited as explainable features to build predictive models.