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Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography.

ABSTRACT: Introduction: Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease.

Methods: A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive (>10) and low negative (<0.01) likelihood ratios to stratify the risk of COVID-19 being present were identified and validated.

Results: The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4 % and 93.3 % and did not differ from both radiologists (p?>?0.05). At the rule-out threshold, sensitivity (100 %) and specificity (60 %) differed significantly from the radiologists (p?
Conclusion: Accurate diagnosis of COVID-19 using a basic deep learning approach is feasible using open-source CT image data. In addition, the machine learning classifier provided validated rule-in and rule-out criteria could be used to stratify the risk of COVID-19 being present.

SUBMITTER: Javor D

PROVIDER: S-EPMC7641539 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography.

Javor D D Kaplan H H Kaplan A A Puchner S B SB Krestan C C Baltzer P P

European journal of radiology 20201104

<h4>Introduction</h4>Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease.<h4>Methods</h4>A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 pati ...[more]

PMID: 33190102

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Project description:PurposeThe novel coronavirus COVID-19, which spread globally in late December 2019, is a global health crisis. Chest computed tomography (CT) has played a pivotal role in providing useful information for clinicians to detect COVID-19. However, segmenting COVID-19-infected regions from chest CT results is challenging. Therefore, it is desirable to develop an efficient tool for automated segmentation of COVID-19 lesions using chest CT. Hence, we aimed to propose 2D deep-learning algorithms to automatically segment COVID-19-infected regions from chest CT slices and evaluate their performance.Material and methodsHerein, 3 known deep learning networks: U-Net, U-Net++, and Res-Unet, were trained from scratch for automated segmenting of COVID-19 lesions using chest CT images. The dataset consists of 20 labelled COVID-19 chest CT volumes. A total of 2112 images were used. The dataset was split into 80% for training and validation and 20% for testing the proposed models. Segmentation performance was assessed using Dice similarity coefficient, average symmetric surface distance (ASSD), mean absolute error (MAE), sensitivity, specificity, and precision.ResultsAll proposed models achieved good performance for COVID-19 lesion segmentation. Compared with Res-Unet, the U-Net and U-Net++ models provided better results, with a mean Dice value of 85.0%. Compared with all models, U-Net gained the highest segmentation performance, with 86.0% sensitivity and 2.22 mm ASSD. The U-Net model obtained 1%, 2%, and 0.66 mm improvement over the Res-Unet model in the Dice, sensitivity, and ASSD, respectively. Compared with Res-Unet, U-Net++ achieved 1%, 2%, 0.1 mm, and 0.23 mm improvement in the Dice, sensitivity, ASSD, and MAE, respectively.ConclusionsOur data indicated that the proposed models achieve an average Dice value greater than 84.0%. Two-dimensional deep learning models were able to accurately segment COVID-19 lesions from chest CT images, assisting the radiologists in faster screening and quantification of the lesion regions for further treatment. Nevertheless, further studies will be required to evaluate the clinical performance and robustness of the proposed models for COVID-19 semantic segmentation.

Project description:ObjectivesTo construct a distribution atlas of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) and further explore the difference in distribution by location and disease severity through a retrospective study of 484 cases in Jiangsu, China.MethodsAll patients diagnosed with COVID-19 from January 10 to February 18 in Jiangsu Province, China, were enrolled in our study. The patients were further divided into asymptomatic/mild, moderate, and severe/critically ill groups. A deep learning algorithm was applied to the anatomic pulmonary segmentation and pneumonia lesion extraction. The frequency of opacity on CT was calculated, and a color-coded distribution atlas was built. A further comparison was made between the upper and lower lungs, between bilateral lungs, and between various severity groups. Additional lesion-based radiomics analysis was performed to ascertain the features associated with the disease severity.ResultsA total of 484 laboratory-confirmed patients with 945 repeated CT scans were included. Pulmonary opacity was mainly distributed in the subpleural and peripheral areas. The distances from the opacity to the nearest parietal/visceral pleura were shortest in the asymptomatic/mild group. More diffused lesions were found in the severe/critically ill group. The frequency of opacity increased with increased severity and peaked at about 3-4 or 7-8 o'clock direction in the upper lungs, as opposed to the 5 or 6 o'clock direction in the lower lungs. Lesions with greater energy, more circle-like, and greater surface area were more likely found in severe/critically ill cases than the others.ConclusionThis study constructed a detailed distribution atlas of COVID-19 pneumonia and compared specific patterns in different parts of the lungs at various severities. The radiomics features most associated with the severity were also found. These results may be valuable in determining the COVID-19 sub-phenotype.Supplementary informationThe online version contains supplementary material available at 10.1007/s43657-021-00011-4.

Dataset Information

Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography.

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Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography.

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