Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction

• Published in: Annual review of biomedical engineering Vol. 24; pp. 179 - 201
• Main Authors: Liu, Tianming, Siegel, Eliot, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States Annual Reviews 06.06.2022
• Subjects: Artificial Intelligence; COVID-19; COVID-19 Testing; Deep Learning; Humans; medical image analysis; medical imaging; radiology; SARS-CoV-2; COVID-19; deep learning; radiology; medical image analysis; medical imaging
• ISSN: 1523-9829; 1545-4274
• DOI: 10.1146/annurev-bioeng-110220-012203

The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to health-care organizations worldwide. To combat the global crisis, the use of thoracic imaging has played a major role in the diagnosis, prediction, and management of COVID-19 patients with moderate to severe symptoms or with evidence of worsening respiratory status. In response, the medical image analysis community acted quickly to develop and disseminate deep learning models and tools to meet the urgent need of managing and interpreting large amounts of COVID-19 imaging data. This review aims to not only summarize existing deep learning and medical image analysis methods but also offer in-depth discussions and recommendations for future investigations. We believe that the wide availability of high-quality, curated, and benchmarked COVID-19 imaging data sets offers the great promise of a transformative test bed to develop, validate, and disseminate novel deep learning methods in the frontiers of data science and artificial intelligence.