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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-306300

##### ABSTRACT

There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. Radiology and nucleic acid test (NAT) are complementary COVID-19 diagnosis methods. In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19. COVID-MTL learns different COVID-19 tasks in parallel through our novel random-weighted loss function, which assigns learning weights under Dirichlet distribution to prevent task dominance;our new 3D real-time augmentation algorithm (Shift3D) introduces space variances for 3D CNN components by shifting low-level feature representations of volumetric inputs in three dimensions;thereby, the MTL framework is able to accelerate convergence and improve joint learning performance compared to single-task models. By only using chest CT scans, COVID-MTL was trained on 930 CT scans and tested on separate 399 cases. COVID-MTL achieved AUCs of 0.939 and 0.846, and accuracies of 90.23% and 79.20% for detection of COVID-19 against radiology and NAT, respectively, which outperformed the state-of-the-art models. Meanwhile, COVID-MTL yielded AUC of 0.800 $\pm$ 0.020 and 0.813 $\pm$ 0.021 (with transfer learning) for classifying control/suspected, mild/regular, and severe/critically-ill cases. To decipher the recognition mechanism, we also identified high-throughput lung features that were significantly related (P < 0.001) to the positivity and severity of COVID-19.

2.
Eur Radiol ; 32(4): 2235-2245, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1606144

##### ABSTRACT

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: â¢ CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. â¢ CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.

##### Subject(s)
Artificial Intelligence , COVID-19 , Algorithms , Humans , Radiologists , Tomography, X-Ray Computed/methods
3.
Pattern Recognit ; 124: 108499, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1562195

##### ABSTRACT

There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. Radiology and nucleic acid test (NAT) are complementary COVID-19 diagnosis methods. In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19. COVID-MTL learns different COVID-19 tasks in parallel through our novel random-weighted loss function, which assigns learning weights under Dirichlet distribution to prevent task dominance; our new 3D real-time augmentation algorithm (Shift3D) introduces space variances for 3D CNN components by shifting low-level feature representations of volumetric inputs in three dimensions; thereby, the MTL framework is able to accelerate convergence and improve joint learning performance compared to single-task models. By only using chest CT scans, COVID-MTL was trained on 930 CT scans and tested on separate 399 cases. COVID-MTL achieved AUCs of 0.939 and 0.846, and accuracies of 90.23% and 79.20% for detection of COVID-19 against radiology and NAT, respectively, which outperformed the state-of-the-art models. Meanwhile, COVID-MTL yielded AUC of 0.800 ± 0.020 and 0.813 ± 0.021 (with transfer learning) for classifying control/suspected, mild/regular, and severe/critically-ill cases. To decipher the recognition mechanism, we also identified high-throughput lung features that were significantly related (P < 0.001) to the positivity and severity of COVID-19.

4.
Biomedical Engineering and Clinical Medicine ; 24(6):678-681, 2020.
Article in Chinese | GIM | ID: covidwho-1456543

##### ABSTRACT

Objective: To improve image quality of bedside digital radiography(DR) in severe and critical patients with corona virus disease 2019(COVID-19).

5.
Nature ; 592(7856): 685, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1213921
6.
SSRN; 2020.
Preprint | SSRN | ID: ppcovidwho-1944
7.
Mol Diagn Ther ; 24(5): 601-609, 2020 10.
Article in English | MEDLINE | ID: covidwho-672021

##### ABSTRACT

BACKGROUND AND OBJECTIVE: Without a specific antiviral treatment or vaccine, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic, affecting over 200 countries worldwide. A better understanding of B- and T-cell immunity is critical to the diagnosis, treatment and prevention of coronavirus disease 2019 (COVID-19). METHODS: A cohort of 129 patients with COVID-19 and 20 suspected cases were enrolled in this study, and a lateral flow immunochromatographic assay (LFIA) and a magnetic chemiluminescence enzyme immunoassay (MCLIA) were evaluated for SARS-CoV-2 IgM/IgG detection. Additionally, 127 patients with COVID-19 were selected for the detection of IgM and IgG antibodies to SARS-CoV-2 to evaluate B-cell immunity, and peripheral blood lymphocyte subsets were quantified in 95 patients with COVID-19 to evaluate T-cell immunity. RESULTS: The sensitivity and specificity of LFIA-IgM/IgG and MCLIA-IgM/IgG assays for detecting SARS-CoV infection were > 90%, comparable with reverse transcription polymerase chain reaction detection. IgM antibody levels peaked on day 13 and began to fall on day 21, while IgG antibody levels peaked on day 17 and were maintained until tracking ended. Lymphocyte and subset enumeration suggested that lymphocytopenia occurred in patients with COVID-19. CONCLUSIONS: LFIA-IgM/IgG and MCLIA-IgM/IgG assays can indicate SARS-CoV-2 infection, which elicits an antibody response. Lymphocytopenia occurs in patients with COVID-19, which possibly weakens the T-cell response.

##### Subject(s)
B-Lymphocytes/immunology , Betacoronavirus/immunology , Coronavirus Infections/immunology , Immunoassay/methods , Pneumonia, Viral/immunology , T-Lymphocytes/immunology , Adolescent , Adult , Aged , Aged, 80 and over , Antibodies, Viral/analysis , Antibodies, Viral/immunology , COVID-19 , Child , Cohort Studies , Female , Humans , Immunoglobulin G/analysis , Immunoglobulin G/immunology , Immunoglobulin M/analysis , Immunoglobulin M/immunology , Lymphocyte Subsets , Male , Middle Aged , Pandemics , SARS-CoV-2 , Young Adult
8.
J Infect ; 81(3): e6-e8, 2020 09.
Article in English | MEDLINE | ID: covidwho-638591
9.
J Infect ; 81(3): e20-e23, 2020 09.
Article in English | MEDLINE | ID: covidwho-635361
10.