@article{Shi_Wang_Abbasi_Wong_2023, title={COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for Early COVID-19 Recommendation}, volume={8}, url={https://openjournals.uwaterloo.ca/index.php/vsl/article/view/5372}, DOI={10.15353/jcvis.v8i1.5372}, abstractNote={<table style="height: 258px;" width="852"> <tbody> <tr> <td width="711">As the COVID-19 pandemic continues to put a significant burden on healthcare systems worldwide, there has been growing interest in finding inexpensive symptom pre-screening and recommendation methods to assist in efficiently using available medical resources such as PCR tests. In this study, we introduce the design of COVID-Net Assistant, an efficient virtual assistant designed to provide symptom prediction and recommendations for COVID-19 by analyzing users’ cough recordings through deep convolutional neural networks. We explore a variety of highly customized, lightweight convolutional neural network architectures generated via machine-driven design exploration (which we refer to as COVID-Net Assistant neural networks) on the Covid19-Cough benchmark dataset. The Covid19-Cough dataset comprises 682 cough recordings from a COVID-19 positive cohort and 642 from a COVID-19 negative cohort. Among the 682 cough recordings labeled positive, 382 recordings were verified by PCR test. Our experimental results show promising, with the COVID-Net Assistant neural networks demonstrating robust predictive performance, achieving AUC scores of over 0.93, with the best score over 0.95 while being fast and efficient in inference. The COVID-Net Assistant models are made available in an open source manner through the COVID-Net open initiative and, while not a production-ready solution, we hope their availability acts as a good resource for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative solutions. </td> </tr> </tbody> </table>}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={Shi, Pengyuan and Wang, Yuetong and Abbasi, Saad and Wong, Alexander}, year={2023}, month={May}, pages={38–41} }