Related Experiment Video
Updated: Nov 18, 2025
08:05
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
14.5K
SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review
Narjes Benameur1, Ramzi Mahmoudi2, Soraya Zaid3
1University of Tunis El Manar, Higher Institute of Medical Technologies of Tunis, Laboratory of Biophysics and Medical Technology, Tunis, Tunisia.
Clinical Imaging
|February 5, 2021
Summary
Computed tomography (CT) is the most accurate imaging technique for diagnosing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). AI tools show promise in detecting SARS-CoV-2 from medical images, aiding pandemic response.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents with diverse and not fully understood clinical features.
- Effective diagnosis is crucial for managing the global health emergency caused by SARS-CoV-2.
Purpose of the Study:
- To review recent medical imaging techniques for SARS-CoV-2 diagnosis.
- To explore the role of artificial intelligence (AI) in SARS-CoV-2 detection and pandemic mitigation.
Main Methods:
- Review of clinical features of SARS-CoV-2 identified through various medical imaging modalities.
- Description of artificial intelligence approaches applied to SARS-CoV-2 diagnosis.
Main Results:
- Computed tomography (CT) is the most accurate modality, with ground-glass opacities and consolidation as common findings.
- Ultrasound reveals B-lines and pleural line irregularities.
- AI, particularly deep learning, shows potential for SARS-CoV-2 detection, though differentiating it from other viral infections remains a challenge.
Conclusions:
- Identifying SARS-CoV-2 on medical images is vital for radiological diagnosis.
- These findings support researchers developing computer-aided diagnosis tools for pulmonary infections.
