A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients (preprint)

Objective: To analyze and compare the imaging workflow, radiation dose and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. Materials and Methods: 127 adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time and off-center distance, of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups.Results: The AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP:1.56cm±0.83 vs. MP: 4.05cm±2.40, p<0.001) and higher proportion of positioning accuracy (AP: 99% vs. MP: 92%), resulted in 16% radiation dose reduction (AP: 6.1mSv±1.3 vs. MP: 7.3mSv±1.2, p<0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas.Conclusion: The AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose, optimizing imaging workflow and image quality in imaging the chest. This technique has important added clinical value in imaging COVID-19 patients to reduce the cross-infection risks.

Scoring Systems for Predicting Mortality for Severe Patients with COVID-19 (preprint)

Background: Coronavirus disease 2019 (COVID-19) has been widely spread and caused tens of thousands of deaths, mainly in patients with severe COVID-19.Methods: Patients with COVID-19 were retrospectively analyzed. Clinical characteristics were compared, and LASSO regression as well as multivariate analysis were used to screen variables and establish prediction model. Findings: A total of 2529 patients with COVID-19 was retrospectively analyzed, and 452 eligible severe COVID-19 were used for finally analysis. In training cohort, the median age was 66·0 years while it was 73·0 years in non-survivors. Patients aged 60-75 years accounted for the largest proportion of infected populations and mortality toll. Anti-SARS-CoV-2 antibodies were monitored up to 54 days, and IgG levels reached the highest during 20-30 days. About 60.2% of severe patients had complications. Acute myocardial injury was the earliest injured organ, whereas the time from acute kidney injury to death was the shortest. Age, diabetes, coronary heart disease (CHD), percentage of lymphocytes (LYM%), procalcitonin (PCT), serum urea, C reactive protein and D-dimer (DD), were identified associated with mortality by LASSO binary logistic regression. Then multivariate analysis was performed to conclude that old age, CHD, LYM%, PCT and DD remained independent risk factors for mortality. Based on the above variables, a scoring system of COVID-19 (CSS) was established and divided into low-risk and high-risk groups. This model displayed good discrimination (AUC=0·919) and calibration (P =0·264). The complications in low-risk and high-risk groups were significantly different. We also found that the use of corticosteroids in low-risk groups increased hospital stays by 4·5 days (P =0·036) and durations of disease by 7·5 days (P =0 · 012) compared with no corticosteroids.Interpretation: Old age, CHD, LYM%, PCT and DD were independently related to mortality. CSS was useful for predicting in-hospital mortality and complications, and it could help clinicians to identify high-risk patients with poor prognosis.Funding Statement: This work was supported by the Key Project for Anti-2019 novel Coronavirus Pneumonia from the Ministry of Science and Technology, China (grant number 2020YFC0845500). Declaration of Interests: All authors declare no competing interests.Ethics Approval Statement: This study was conducted according to the principles of Helsinki and approved by the Ethics Committee of Zhongnan Hospital of Wuhan University (No.2020063).

Radiological Features on HRCT and RT-PCR Testing for the Diagnosis of Coronavirus Disease 2019 (COVID-19) in China: A Comparative Study of 78 Cases in Pregnant Women (preprint)

BackgroundIn the past four months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global health threat. In the context of the coronavirus disease 2019 (COVID-19) epidemic, pneumonia is a critical disease that threatens the health of pregnant women and fetuses. We aimed to evaluate the quantitative parameters of CT scans performed on pregnant women with COVID-19 who had different reverse transcription-polymerase chain reaction (RT-PCR) results.MethodsPregnant women with suspected cases of COVID-19 pneumonia (confirmed by next-generation sequencing or RT-PCR) who underwent high-resolution lung CT scans were retrospectively enrolled. Patients were grouped based on the results of the RT-PCR and the first CT scan: group 1 (double positive patients; positive RT-PCR and CT scan) and group 2 (negative RT-PCR and positive CT scan). The imaging features and their distributions were extracted and compared between the two groups.ResultsSeventy-eight patients were admitted to the hospital between Dec 20, 2019, and Feb 29, 2020. The mean age of the patients was 31.82 years (SD 4.1, ranged from 21 to 46 years). The cohort included 14 (17.95%) patients with a positive RT-PCR test and 64 (82.05%) with a negative RT-PCR test, there were 37 (47.44%) patients with a positive CT scan, and 41 (52.56%) patients with a negative CT scan. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of CT-based diagnosis of COVID-19 were 85.71%, 60.94%, 32.40%, 95.12% and 65.38%, respectively. COVID-19 pneumonia mainly involved the right lower lobe of the lung. There were 53 semi-quantitative and 59 quantitative parameters, which were compared between the two groups. There were no significant differences in the quantitative parameters. However, the Hellinger distance was significantly different between the two groups, albeit with a limited diagnostic value (AUC = 0.63).ConclusionsPregnant women with pneumonia usually present with typical abnormal signs on CT. Although multidimensional CT quantitative parameters are somewhat different between groups of patients with different RT-PCR results, it is still impossible to accurately predict whether the RT-PCR will be positive, which would allow for the earlier detection of SARS-CoV-2 infection.

Radiological Features on HRCT and RT-PCR Testing for the Diagnosis of Coronavirus Disease 2019 (COVID-19) in China: A Comparative Study of 78 Cases in Pregnant Women (preprint)

Objective: We aimed to evaluate the quantitative parameters of CT scans performed on pregnant women with COVID-19 who had different reverse transcription-polymerase chain reaction (RT-PCR) results.Methods: Pregnant women with suspected cases of COVID-19 pneumonia (confirmed by next-generation sequencing or RT-PCR) who underwent high-resolution lung CT scans were retrospectively enrolled. Patients were grouped based on the results of the RT-PCR and the first CT scan: group 1 (double positive patients; positive RT-PCR and CT scan) and group 2 (negative RT-PCR and positive CT scan). The imaging features and their distributions were extracted and compared between the two groups.Results: Seventy-eight patients were admitted to the hospital between Dec 20, 2019, and Feb 29, 2020. The mean age of the patients was 31.82 years (SD 4.1, ranged from 21 to 46 years). The cohort included 14 (17.95%) patients with a positive RT-PCR test and 64 (82.05%) with a negative RT-PCR test, there were 37 (47.44%) patients with a positive CT scan, and 41 (52.56%) patients with a negative CT scan. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of CT-based diagnosis of COVID-19 were 85.71%, 60.94%, 32.40%, 95.12% and 65.38%, respectively. COVID-19 pneumonia mainly involved the right lower lobe of the lung. There were 53 semi-quantitative and 59 quantitative parameters, which were compared between the two groups. There were no significant differences in the quantitative parameters. However, the Hellinger distance was significantly different between the two groups, albeit with a limited diagnostic value (AUC=0.63).Conclusions: Pregnant women with pneumonia usually present with typical abnormal signs on CT. Although multidimensional CT quantitative parameters are somewhat different between groups of patients with different RT-PCR results, it is still impossible to accurately predict whether the RT-PCR will be positive, which would allow for the earlier detection of SARS-CoV-2 infection.

AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks (preprint)

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hoped artificial intelligence (AI) to help reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. Here, we present our experience in building and deploying an AI system that automatically analyzes CT images to detect COVID-19 pneumonia features. Different from conventional medical AI, we were dealing with an epidemic crisis. Working in an interdisciplinary team of over 30 people with medical and / or AI background, geographically distributed in Beijing and Wuhan, we were able to overcome a series of challenges in this particular situation and deploy the system in four weeks. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we were able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. Besides, the system automatically highlighted all lesion regions for faster examination. As of today, we have deployed the system in 16 hospitals, and it is performing over 1,300 screenings per day.

Value of chest CT screening in the early COVID-19 outbreak / 中华放射学杂志

Objective@#In view of the difficulty of the shortage of new coronavirus nucleal acid test in the early COVID-19 outbreak, to explore the application value of chest CT in screening COVID-19 patients.@*Methods@#Retrospective analysis was performed on the data of patients with fever who received chest CT and new coronavirus nucleal acid test during January 25, 2020 to February 2, 2020 in Zhongnan Hospital of Wuhan University. A total of 587 patients were enrolled, including 290 males and 297 females, aged from 11.0 to 96.0 (51.3±17.1) years old. Take the nucleic acid test results as the gold standard, the sensitivity, specificity and rate of missed diagnosis of CT screening COVID-19 were calculated.@*Results@#Among the 587 patients, there were 433 positive cases (73.8%, 433/587) and 154 negative cases (26.2%, 154/587) of novel coronavirus nucleic acid test. Using CT screening, 494 cases (84.2%, 494/587) were positive and 93 cases (15.8%, 93/587) were negative. The sensitivity of CT screening COVID-19 was 97.7% (423/433), specificity was 53.9% (83/154) and rate of missed diagnosis was 2.3% (10/433).@*Conclusions@#In the early COVID-19 outbreak, CT screening has the advantages of high sensitivity and low rate of missed diagnosis of COVID-19, which can compensate for the shortage of new coronavirus nucleal acid test and can be used as the basis for rapid screening for early prevention and control of COVID-19 outbreak.

Clinical course and outcome of 107 patients infected with the novel coronavirus, SARS-CoV-2, discharged from two hospitals in Wuhan, China. (preprint)

Background In December 2019, Coronavirus Disease 2019 (COVID-19) outbreak was reported from Wuhan, China. Information on the clinical course and prognosis of COVID-19 was not thoroughly described. We described the clinical courses and prognosis in COVID-19 patients. Methods Retrospective case series of COVID-19 patients from Zhongnan Hospital of Wuhan University in Wuhan, and Xi-shui Hospital, Hubei Province, China, up to February 10, 2020. Epidemiological, demographic and clinical data were collected. Clinical course of survivors and non-survivors were compared. Risk factors for death were analyzed. Results A total of 107 discharged patients with COVID-19 were enrolled. The clinical course of COVID-19 presented as a tri-phasic pattern. Week 1 after illness onset was characterized by fever, cough, dyspnea, lymphopenia and radiological multilobar pulmonary infiltrates. In severe cases, thrombocytopenia, acute kidney injury, acute myocardial injury or adult respiratory distress syndrome were observed. During week 2, in mild cases, fever, cough and systemic symptoms began to resolve and platelet count rose to normal range, but lymphopenia persisted. In severe cases, leukocytosis, neutrophilia and deteriorating multi-organ dysfunction were dominant. By week 3, mild cases had clinically resolved except for lymphopenia. However, severe cases showed persistent lymphopenia, severe acute respiratory dyspnea syndrome , refractory shock, anuric acute kidney injury, coagulopathy, thrombocytopenia and death. Older age and male sex were independent risk factors for poor outcome of the illness. Conclusions A period of 7–13 days after illness onset is the critical stage in COVID-19 course. Age and male gender were independent risk factors for death of COVID-19.

Pulmonary Pathology of Early Phase 2019 Novel Coronavirus (COVID-19) Pneumonia in Two Patients with Lung Cancer (preprint)

There is currently a lack of pathologic data on the novel coronavirus (SARS-CoV-2) pneumonia, or COVID-19, from autopsy or biopsy. Two patients who recently underwent lung lobectomies for adenocarcinoma were retrospectively found to have had COVID-19 at the time of surgery. These two cases thus provide important first opportunities to study the pathology of COVID-19. Pathologic examinations revealed that, apart from the tumors, the lungs of both patients exhibited edema, proteinaceous exudate, focal reactive hyperplasia of pneumocytes with patchy inflammatory cellular infiltration, and multinucleated giant cells. Hyaline membranes were not prominent. Since both patients did not exhibit symptoms of pneumonia at the time of surgery, these changes likely represent an early phase of the lung pathology of COVID-19 pneumonia.