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1.
Eur Radiol ; 32(1): 205-212, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1293361

ABSTRACT

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.


Subject(s)
COVID-19 , Artificial Intelligence , Humans , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
3.
BMC Infect Dis ; 20(1): 644, 2020 Sep 01.
Article in English | MEDLINE | ID: covidwho-740367

ABSTRACT

BACKGROUND: To explore the clinical features and CT findings of clinically cured coronavirus disease 2019 (COVID-19) patients with viral RNA positive anal swab results after discharge. METHODS: Forty-two patients with COVID-19 who were admitted to Yongzhou Central Hospital, Hunan, China, between January 20, 2020, and March 2, 2020, were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using anal swab viral RT-PCR. In this report, we present the clinical characteristics and chest CT features of six patients with positive anal swab results and compare the clinical, laboratory, and CT findings between the positive and negative groups. RESULTS: The anal swab positivity rate for SARS-CoV-2 RNA in discharged patients was 14.3% (6/42). All six patients were male. In the positive group, 40% of the patients (2/5) had a positive stool occult blood test (OBT), but none had diarrhea. The median duration of fever and major symptoms (except fever) in the positive patients was shorter than that of the negative patients (1 day vs. 6 days, 4.5 days vs. 10.5 days, respectively). The incidence of asymptomatic cases in the positive group (33.3%) was also higher than that of the negative group (5.6%). There were no significant differences in the CT manifestation or evolution of the pulmonary lesions between the two groups. CONCLUSION: In our case series, patients with viral RNA positive anal swabs did not exhibit gastrointestinal symptoms, and their main symptoms disappeared early. They had similar CT features to the negative patients, which may be easier to be ignored. A positive OBT may indicate gastrointestinal damage caused by SARS-CoV-2 infection.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/diagnostic imaging , Patient Discharge/statistics & numerical data , Pneumonia, Viral/diagnostic imaging , RNA, Viral/analysis , Severe Acute Respiratory Syndrome/diagnostic imaging , Adolescent , Adult , Aged , Anal Canal/virology , Betacoronavirus/genetics , COVID-19 , Child , Child, Preschool , China/epidemiology , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Fever , Hospitalization , Hospitals , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2 , Severe Acute Respiratory Syndrome/epidemiology , Severe Acute Respiratory Syndrome/virology , Tomography, X-Ray Computed , Young Adult
4.
Radiology ; 296(3): E156-E165, 2020 09.
Article in English | MEDLINE | ID: covidwho-729427

ABSTRACT

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.


Subject(s)
Artificial Intelligence , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Radiologists , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , Aged, 80 and over , Betacoronavirus , COVID-19 , Child , Child, Preschool , China , Diagnosis, Differential , Female , Humans , Infant , Infant, Newborn , Lung/diagnostic imaging , Male , Middle Aged , Pandemics , Philadelphia , Pneumonia/diagnostic imaging , Radiography, Thoracic , Radiologists/standards , Radiologists/statistics & numerical data , Retrospective Studies , Rhode Island , SARS-CoV-2 , Sensitivity and Specificity , Young Adult
5.
Radiology ; 296(2): E46-E54, 2020 08.
Article in English | MEDLINE | ID: covidwho-697192

ABSTRACT

Background Despite its high sensitivity in diagnosing coronavirus disease 2019 (COVID-19) in a screening population, the chest CT appearance of COVID-19 pneumonia is thought to be nonspecific. Purpose To assess the performance of radiologists in the United States and China in differentiating COVID-19 from viral pneumonia at chest CT. Materials and Methods In this study, 219 patients with positive COVID-19, as determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings, were retrospectively identified from seven Chinese hospitals in Hunan Province, China, from January 6 to February 20, 2020. Two hundred five patients with positive respiratory pathogen panel results for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia, according to original radiologic interpretation within 7 days of each other, were identified from Rhode Island Hospital in Providence, RI. Three radiologists from China reviewed all chest CT scans (n = 424) blinded to RT-PCR findings to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched patients was randomly selected and evaluated by four radiologists from the United States in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CT scans (n = 424), the accuracy of the three radiologists from China in differentiating COVID-19 from non-COVID-19 viral pneumonia was 83% (350 of 424), 80% (338 of 424), and 60% (255 of 424). In the randomly selected sample (n = 58), the sensitivities of three radiologists from China and four radiologists from the United States were 80%, 67%, 97%, 93%, 83%, 73%, and 70%, respectively. The corresponding specificities of the same readers were 100%, 93%, 7%, 100%, 93%, 93%, and 100%, respectively. Compared with non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs 57%, P < .001), ground-glass opacity (91% vs 68%, P < .001), fine reticular opacity (56% vs 22%, P < .001), and vascular thickening (59% vs 22%, P < .001), but it was less likely to have a central and peripheral distribution (14% vs 35%, P < .001), pleural effusion (4% vs 39%, P < .001), or lymphadenopathy (3% vs 10%, P = .002). Conclusion Radiologists in China and in the United States distinguished coronavirus disease 2019 from viral pneumonia at chest CT with moderate to high accuracy. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.


Subject(s)
Betacoronavirus , Clinical Competence , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Radiologists/standards , Adult , Aged , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Coronavirus Infections/pathology , Diagnosis, Differential , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/pathology , Pneumonia, Viral/virology , Predictive Value of Tests , Retrospective Studies , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2 , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
6.
Zhong Nan Da Xue Xue Bao Yi Xue Ban ; 45(3): 269-274, 2020 Mar 28.
Article in English, Chinese | MEDLINE | ID: covidwho-215638

ABSTRACT

The coronavirus disease 2019 (COVID-19) has attracted extensive attention all around the world recently. Early screening, early diagnosis, early isolation, and early treatment remain the most effective prevention and control measures. Computed tomography (CT) plays a vital role in the screening, diagnosis, treatment, and follow-up of COVID-19, especially in the early screening, with a higher sensitivity than that of real-time fluorescence RT-PCR. The combination of CT and artificial intelligence has the potential to help clinicians in improving the diagnostic accuracy and working efficiency.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Artificial Intelligence , COVID-19 , Humans , Pandemics , SARS-CoV-2 , Tomography, X-Ray Computed
7.
Zhong Nan Da Xue Xue Bao Yi Xue Ban ; 45(3): 257-261, 2020 Mar 28.
Article in English, Chinese | MEDLINE | ID: covidwho-215565

ABSTRACT

OBJECTIVES: To investigate the role of chest CT for the diagnostic work-up for patients with suspected infection of coronavirus disease 2019 (COVID-19). METHODS: The clinical data and imaging findings of the first nucleic acid-negative COVID-19 patients were analyzed and compared with the first nucleic acid-positive patients. RESULTS: Compared with the first nucleic acid-positive patients, the onset time of the first nucleic acid-negative patients was shorter [(3.58±2.94) d], but the diagnosis was longer [(3.92±3.66) d]. There were no significant differences in the characteristics of the clinical data and radiological findings between the 2 groups (P>0.05). CONCLUSION: Chest CT examination is important to avoid COVID-19 missed diagnosis due to false negative nucleic acid.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , COVID-19 , Humans , Pandemics , SARS-CoV-2 , Tomography, X-Ray Computed
8.
Zhong Nan Da Xue Xue Bao Yi Xue Ban ; 45(3): 275-279, 2020 Mar 28.
Article in English, Chinese | MEDLINE | ID: covidwho-215397

ABSTRACT

Although continuous outbreak of coronavirus disease 2019, it has been widely reported, there were few reports regarding family cases. We reported a group of family cluster outbreak cases confirmed in Xiangya Hospital, Central South University, China, and their clinical and image characteristics have been analyzed in order to provide reference for the prevention and early diagnosis of this disease. A total of 5 patients from one family, including 4 adults and 1 child, had a history of human contact in Wuhan, Hubei. Four adult patients showed different symptoms, including cough, fever, pharyngeal pain, and dyspnea, while the child patient had no symptoms. Laboratory examination showed no abnormality in all the patients except for slight increase in CRP in 2 patients and mild abnormal liver function index in 1 patient. The chest CT showed that all patients had abnormal images, with different degrees of manifestations.All patients were finally diagnosed by the nucleic acid test.


Subject(s)
Betacoronavirus , Coronavirus Infections , Pandemics , Pneumonia, Viral , Adult , COVID-19 , Child , China , Coronavirus Infections/epidemiology , Disease Outbreaks , Humans , Pneumonia, Viral/epidemiology , SARS-CoV-2
9.
Zhong Nan Da Xue Xue Bao Yi Xue Ban ; 45(3): 229-235, 2020 Mar 28.
Article in English, Chinese | MEDLINE | ID: covidwho-211133

ABSTRACT

OBJECTIVES: To design a standardized imaging diagnostic reporting mode for screening coronavirus disease 2019 (COVID-19), and to prospectively verify its effectiveness in clinical practice. METHODS: A new classification and standardized imaging diagnosis report mode of viral pneumonia was established by studying and summarizing the imaging findings of various kinds of viral pneumonia, combining with lesion density, interstitial changes, pleural effusion, lymph nodes, and some special signs. After systematic training, the radiologist experienced clinical practice for screening CT features. COVID-19 cases were screened retrospectively in the single-center. The confirmed cases were verified, and the diagnostic efficacy of the standardized imaging reporting system in screening COVID-19 was tested. RESULTS: There were 912 patients in this stage receiving the screening imaging examination. Of them, 190 patients were screened in the report mode and 30 patients were diagnosed as COVID-19. The CT manifestation of COVID-19 was characterized by pure ground glass lesions or with a few solid components, predominant subpleural distribution, no lymph node enlargement and pleural effusion, and often with paving-way sign and air bronchus sign. In combination with the above signs, the diagnostic efficacy of COVID-19 was 0.942. CONCLUSIONS: The standardized imaging diagnosis report mode based on COVID-19 chest image features is effective and practical, which should be popularized.


Subject(s)
Betacoronavirus , COVID-19 , Coronavirus Infections/diagnosis , Humans , Pandemics , Pneumonia, Viral/diagnosis , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
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