AI-Assisted Diagnosis for Covid-19 CXR Screening: From Data Collection to Clinical Validation
<|reference_start|>AI-Assisted Diagnosis for Covid-19 CXR Screening: From Data Collection to Clinical Validation: 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/<|reference_end|>
@article{barbano2024ai-assisted,
    title={AI-Assisted Diagnosis for Covid-19 CXR Screening: From Data Collection
  to Clinical Validation},
    author={Carlo Alberto Barbano, Riccardo Renzulli, Marco Grosso, Domenico
  Basile, Marco Busso, Marco Grangetto},
    journal={arXiv preprint arXiv:2405.11598},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11598},
    primaryClass={eess.IV cs.AI cs.CV}
}