How Might AI and Chest Imaging Help Unravel COVID-19’s                     Mysteries?

Kundu, Shinjini; Elhalawani, Hesham; Gichoya, Judy Wawira; Kahn, Charles Η.

doi:10.1148/ryai.2020200053

Cited by 61 publications

(81 citation statements)

References 32 publications

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“…Most AI studies on chest CT used for differentiating COVID-19 pneumonia from other causes of pneumonia consider both three-class classification problems (COVID-19 pneumonia, non-COVID-19 pneumonia, and healthy) and two-class classification (COVID-19 pneumonia and healthy) 2 . Due to the limit of publicly available data, this study concerns with the two-class classification.…”

Section: Discussionmentioning

confidence: 99%

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

Pham

2020

Sci Rep

124

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The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult and can take a long time to be recognized by radiologists. Artificial intelligence methods for automated classification of COVID-19 on CT scans have been found to be very promising. However, current investigation of pretrained convolutional neural networks (CNNs) for COVID-19 diagnosis using CT data is limited. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Among the 16 CNNs, DenseNet-201, which is the deepest net, is the best in terms of accuracy, balance between sensitivity and specificity, $$F_1$$ F 1 score, and area under curve. Furthermore, the implementation of transfer learning with the direct input of whole image slices and without the use of data augmentation provided better classification rates than the use of data augmentation. Such a finding alleviates the task of data augmentation and manual extraction of regions of interest on CT images, which are adopted by current implementation of deep-learning models for COVID-19 classification.

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Section: Discussionmentioning

confidence: 99%

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

Pham

2020

Sci Rep

124

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“…With the aid of AI-rad -an artificial intelligence based lesion detection tool they could classify mild and non-mild cases with a sensitivity of up to 90%. The use of AI based lesion detection tools have indeed played a key role for predicting several outcomes like severity and disease progression in COVID-19 [25]. Although a number of published articles have provided severity assessment, very little research is being done to address issues emerging from images sourced from multiple radio-diagnostic centers and hospitals.…”

Section: Review Of Literaturementioning

confidence: 99%

A Quantitative Lung Computed Tomography Image Feature for Multi-Center Severity Assessment of COVID-19

Ghosh

Kumar

Sadhu

et al. 2020

Preprint

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The COVID-19 pandemic has affected millions and congested healthcare systems globally. Hence an objective severity assessment is crucial in making therapeutic decisions judiciously. Computed Tomography (CT)-scans can provide demarcating features to identify the severity of pneumonia, commonly associated with COVID-19, in the affected lungs. Here, a semi-quantitative severity assessing lung CT image feature is demonstrated for COVID-19 patients. An open-source multi-center Italian database was used, among which 61 cases were incorporated in the study (age 27-86, 71% males) from 27 CT imaging centers. Lesions in the form of opacifications, crazy-paving patterns, and consolidations were heuristically marked and the severity determining feature L_norm was quantified and established to be statistically distinct for the three classes i.e., mild, moderate, and severe (p-value < 0.0001). The thresholds of L_norm for a 3-class classification were determined based on the optimum sensitivity/specificity combination from ROC analyses. The feature L_norm classified the cases in the three severity categories with 89.34% accuracy when L_norm was calculated based on experts' heuristic annotations. Substantial to almost-perfect intra-rater and inter-rater agreements were achieved involving expert and non-expert based evaluations (κ-score 0.79-0.97). We trained machine learning based classification models and showed L_norm alone has a superior diagnostic accuracy over standard image intensity and texture features. Classification accuracy was further increased when L_norm was used to delineate the severe cases from non-severe ones (98.9%) with high sensitivity (97.7%), and specificity (97.49%). Therefore, key highlights of this severity assessment feature are accuracy, lower dependency on expert availability, and wide utility across multiple imaging centers.

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“…Artificial Intelligence (AI) using deep learning has been advocated for automated reading of COVID-19 CT scans, including diagnosing COVID-19 (7)(8)(9)(10)(11)(12)(13)(14) and quantifying parenchymal involvement (15)(16)(17)(18). While these studies illustrate the potential of AI algorithms, their practical value is debatable (19). Without adhering to radiological reporting standards, it is doubtful whether these algorithms provide any real benefit in addition to or instead of manual reading, limiting their adoption in daily practice.…”

Section: N P R E S S Introductionmentioning

confidence: 99%

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

et al. 2021

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How Might AI and Chest Imaging Help Unravel COVID-19’s Mysteries?

Cited by 61 publications

References 32 publications

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

A Quantitative Lung Computed Tomography Image Feature for Multi-Center Severity Assessment of COVID-19

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

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