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Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction
Tianming Liu1, Eliot Siegel2, Dinggang Shen3,4
1Department of Computer Science, University of Georgia, Athens, Georgia, USA;
Annual Review of Biomedical Engineering
|March 22, 2022
Summary
Deep learning models analyze thoracic imaging for coronavirus disease 2019 (COVID-19) diagnosis and management. High-quality COVID-19 imaging datasets are crucial for developing advanced artificial intelligence tools.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Data Science
Background:
- The COVID-19 pandemic presented significant challenges to global healthcare.
- Thoracic imaging is vital for diagnosing, predicting, and managing COVID-19 patients.
Purpose of the Study:
- To review deep learning and medical image analysis methods for COVID-19.
- To provide recommendations for future research in AI for medical imaging.
Main Methods:
- Review of existing deep learning models for COVID-19 medical image analysis.
- Discussion of challenges and opportunities in data availability and quality.
Main Results:
- Rapid development of deep learning tools for interpreting COVID-19 imaging data.
- Identification of the need for curated, benchmarked datasets.
Conclusions:
- High-quality, curated COVID-19 imaging datasets are essential for validating and disseminating AI methods.
- AI and data science hold transformative potential for medical imaging analysis in infectious diseases.
