Computer-aided diagnosis (CAD) systems stand out as potent aids for physicians in identifying the novel Coronavirus Disease 2019 (COVID-19) through medical imaging modalities. In this paper, we showcase the integration and reliable and fast deployment of a state-of-the-art AI system designed to automatically analyze CT images, offering infection probability for the swift detection of COVID-19. The suggested system, comprising both classification and segmentation components, is anticipated to reduce physicians' detection time and enhance the overall efficiency of COVID-19 detection. We successfully surmounted various challenges, such as data discrepancy and anonymisation, testing the time-effectiveness of the model, and data security, enabling reliable and scalable deployment of the system on both cloud and edge environments. Additionally, our AI system assigns a probability of infection to each 3D CT scan and enhances explainability through anchor set similarity, facilitating timely confirmation and segregation of infected patients by physicians.
@article{arxiv.2403.06242,
title = {COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System},
author = {Demetris Gerogiannis and Anastasios Arsenos and Dimitrios Kollias and Dimitris Nikitopoulos and Stefanos Kollias},
journal= {arXiv preprint arXiv:2403.06242},
year = {2024}
}