AI-Assisted Diagnosis for Covid-19 CXR Screening: From Data Collection to Clinical Validation
This work addresses the need for efficient Covid-19 screening tools for radiologists, but it is incremental as it builds on existing AI methods for medical imaging.
The paper tackles the problem of diagnosing Covid-19 pneumonia from chest X-ray images by developing an AI-assisted system, resulting in a public dataset, a deep learning pipeline with debiasing, and clinical validation showing benefits in accuracy and time efficiency.
In this paper, we present the major results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a state-of-the-art AI-based system for diagnosing Covid-19 pneumonia from Chest X-ray (CXR) images. The contributions of this work are manyfold: the release of the public CORDA dataset, a deep learning pipeline for Covid-19 detection, and the clinical validation of the developed solution by expert radiologists. The proposed detection model is based on a two-step approach that, paired with state-of-the-art debiasing, provides reliable results. Most importantly, our investigation includes the actual usage of the diagnosis aid tool by radiologists, allowing us to assess the real benefits in terms of accuracy and time efficiency. Project homepage: https://corsa.di.unito.it/