☆ 4.7 Review

Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

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Summary: In this study, transfer learning technique was applied to clinical images of different pulmonary diseases including COVID-19, revealing the similarity between COVID-19 and pneumonia lung disease, and proposing a model for detecting COVID-19. Additionally, texture feature extraction was utilized for faster detection of COVID-19, aiming to predict cases early to control disease spread effectively.

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Summary: This review examined the sensitivity, specificity, and feasibility of chest CT in detecting COVID-19 compared with RT-PCR. Results showed high sensitivity (67-100%) and relatively low specificity (25-80%) for CT scans, while RT-PCR had modest sensitivity (53-88%). A reverse calculation approach indicated that CT could have higher specificity (83-100%) if considering the modest sensitivity of RT-PCR. Arguments were presented for the added value of chest CT scans in diagnosing COVID-19, especially in patients with typical symptoms and negative RT-PCR results in highly infected regions.

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Summary: This paper presents the experience of building and deploying an AI system for rapid detection of COVID-19 pneumonia, which can save time for physicians and improve the performance of COVID-19 detection. The authors overcame various challenges in a interdisciplinary team and successfully deployed the system in four weeks.

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Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism

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Kai Gao et al.

Summary: Developing accurate and efficient diagnostic tools for COVID-19 is crucial due to limited medical resources, and AI-aided tools have shown promise. A novel dual-branch combination network (DCN) was developed for COVID-19 diagnosis, achieving high classification accuracy and sensitivity in lesion detection. The DCN also provides good interpretability on infection loci and an online CT-based diagnostic platform for COVID-19 is now available.

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Jun Ma et al.

Summary: This study establishes three benchmarks for COVID-19 CT segmentation, including areas such as few-shot learning, domain generalization, and knowledge transfer, and provides standard training, validation, and testing splits for a fair comparison among different methods. By offering over 40 pretrained baseline models, high average Dice similarity coefficient and normalized surface Dice scores were achieved, showcasing efficient deep learning methods for COVID-19 lung and infection segmentation.

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The RSNA International COVID-19 Open Radiology Database (RICORD)

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Summary: The COVID-19 pandemic is a global health emergency, and chest X-rays and CT scans play a vital role in detecting and managing infected patients. The RSNA and Society of Thoracic Radiology collaborated to develop the RICORD database, which provides annotated COVID-19 imaging data for research and education.

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Ying Song et al.

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Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

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Deep learning for diagnosis of COVID-19 using 3D CT scans

Sertan Serte et al.

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Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images

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Xiaocong Chen et al.

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Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images

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COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning

Parnian Afshar et al.

Summary: COVID-19 has significantly impacted over 200 countries worldwide, leading to the emergence of a new CT scan dataset, COVID-CT-MD, which shows potential in advancing research and developing advanced ML and DNN solutions for COVID-19.

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Summary: This article presents a new automatic segmentation method for lung lesions from COVID-19 CT images based on a 3-D network, which was evaluated through experiments to have high performance.

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IEEE TRANSACTIONS ON IMAGE PROCESSING (2008)

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