CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning
This work addresses the need for automated, robust interpretation of CT scans to aid in treatment planning and patient prioritization for COVID-19, representing a domain-specific advancement.
The authors tackled the problem of analyzing chest CT scans to detect and quantify lung lesions in COVID-19 patients, using an ensemble of deep convolutional neural networks for segmentation and classification. Their model outperformed most radiologists in segmentation and all radiologists in disease severity classification.
Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19.Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and prioritizingpatients by severity of the disease. In this paper we adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of slices of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage. Our modelswere trained on data from different medical centers. We compared predictions of our models with those of six experiencedradiologists and our segmentation model outperformed most of them. On the task of classification of disease severity, ourmodel outperformed all the radiologists.