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1.
Radiol Artif Intell ; 4(5): e210315, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-2053381

ABSTRACT

Purpose: To demonstrate the value of pretraining with millions of radiologic images compared with ImageNet photographic images on downstream medical applications when using transfer learning. Materials and Methods: This retrospective study included patients who underwent a radiologic study between 2005 and 2020 at an outpatient imaging facility. Key images and associated labels from the studies were retrospectively extracted from the original study interpretation. These images were used for RadImageNet model training with random weight initiation. The RadImageNet models were compared with ImageNet models using the area under the receiver operating characteristic curve (AUC) for eight classification tasks and using Dice scores for two segmentation problems. Results: The RadImageNet database consists of 1.35 million annotated medical images in 131 872 patients who underwent CT, MRI, and US for musculoskeletal, neurologic, oncologic, gastrointestinal, endocrine, abdominal, and pulmonary pathologic conditions. For transfer learning tasks on small datasets-thyroid nodules (US), breast masses (US), anterior cruciate ligament injuries (MRI), and meniscal tears (MRI)-the RadImageNet models demonstrated a significant advantage (P < .001) to ImageNet models (9.4%, 4.0%, 4.8%, and 4.5% AUC improvements, respectively). For larger datasets-pneumonia (chest radiography), COVID-19 (CT), SARS-CoV-2 (CT), and intracranial hemorrhage (CT)-the RadImageNet models also illustrated improved AUC (P < .001) by 1.9%, 6.1%, 1.7%, and 0.9%, respectively. Additionally, lesion localizations of the RadImageNet models were improved by 64.6% and 16.4% on thyroid and breast US datasets, respectively. Conclusion: RadImageNet pretrained models demonstrated better interpretability compared with ImageNet models, especially for smaller radiologic datasets.Keywords: CT, MR Imaging, US, Head/Neck, Thorax, Brain/Brain Stem, Evidence-based Medicine, Computer Applications-General (Informatics) Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by Cadrin-Chênevert in this issue.

2.
Radiology ; 295(3): 200463, 2020 06.
Article in English | MEDLINE | ID: covidwho-1723927

ABSTRACT

In this retrospective study, chest CTs of 121 symptomatic patients infected with coronavirus disease-19 (COVID-19) from four centers in China from January 18, 2020 to February 2, 2020 were reviewed for common CT findings in relationship to the time between symptom onset and the initial CT scan (i.e. early, 0-2 days (36 patients), intermediate 3-5 days (33 patients), late 6-12 days (25 patients)). The hallmarks of COVID-19 infection on imaging were bilateral and peripheral ground-glass and consolidative pulmonary opacities. Notably, 20/36 (56%) of early patients had a normal CT. With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, "crazy-paving" pattern and the "reverse halo" sign. Bilateral lung involvement was observed in 10/36 early patients (28%), 25/33 intermediate patients (76%), and 22/25 late patients (88%).


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung Diseases/diagnostic imaging , Lung Diseases/virology , Pneumonia, Viral/diagnostic imaging , Adolescent , Adult , Aged , Aged, 80 and over , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Female , Humans , Lung/diagnostic imaging , Lung/pathology , Lung/virology , Lung Diseases/pathology , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Radiography, Thoracic/methods , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods , Young Adult
3.
Radiol Case Rep ; 15(9): 1633-1637, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-634536

ABSTRACT

Since the outbreak of the ongoing pandemic of the novel coronavirus disease (COVID-19) in Wuhan, China, from December 2019, we have learned that multiple organs can be affected with the potential for various complications. Although myalgia is a frequent symptom in COVID-19 patients, no imaging findings of rhabdomyolysis have been featured in the literature. We report a case of presumed rhabdomyolysis in a 38-year-old male with COVID-19 based on the clinical presentation, laboratory results and radiological findings. By discussing the diagnostic rationale and reviewing the relevant literature we hope to advance the existing understanding of this disease and its effects on the musculoskeletal system.

4.
Journal of Thoracic Imaging ; Publish Ahead of Print, 2020.
Article | WHO COVID | ID: covidwho-275190

ABSTRACT

Coronavirus Disease 2019 (COVID-19) pneumonia has become a global pandemic. Although the rate of new infections in China has decreased, currently, 169 countries report confirmed cases, with many nations showing increasing numbers daily. Testing for COVID-19 infection is performed via reverse transcriptase polymerase chain reaction, but availability is limited in many parts of the world. The role of chest computed tomography is yet to be determined and may vary depending on the local prevalence of disease and availability of laboratory testing. A common but nonspecific pattern of disease with a somewhat predictable progression is seen in patients with COVID-19. Specifically, patchy ground-glass opacities in the periphery of the lower lungs may be present initially, eventually undergoing coalescence, consolidation, and organization, and ultimately showing features of fibrosis. In this article, we review the computed tomography features of COVID-19 infection. Familiarity with these findings and their evolution will help radiologists recognize potential COVID-19 and recognize the significant overlap with other causes of acute lung injury. The authors declare no conflicts of interest Correspondence to: Nikhil Goyal, MD, Department of Radiology, Northwell Health System, 300 Community Drive, Manhasset, NY 11040 (e-mail: ngoyal@northwell.edu). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved

5.
Eur Radiol ; 30(8): 4407-4416, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-15134

ABSTRACT

OBJECTIVES: To explore the relationship between the imaging manifestations and clinical classification of COVID-19. METHODS: We conducted a retrospective single-center study on patients with COVID-19 from Jan. 18, 2020 to Feb. 7, 2020 in Zhuhai, China. Patients were divided into 3 types based on Chinese guideline: mild (patients with minimal symptoms and negative CT findings), common, and severe-critical (patients with positive CT findings and different extent of clinical manifestations). CT visual quantitative evaluation was based on summing up the acute lung inflammatory lesions involving each lobe, which was scored as 0 (0%), 1 (1-25%), 2 (26-50%), 3 (51-75%), or 4 (76-100%), respectively. The total severity score (TSS) was reached by summing the five lobe scores. The consistency of two observers was evaluated. The TSS was compared with the clinical classification. ROC was used to test the diagnosis ability of TSS for severe-critical type. RESULTS: This study included 78 patients, 38 males and 40 females. There were 24 mild (30.8%), 46 common (59.0%), and 8 severe-critical (10.2%) cases, respectively. The median TSS of severe-critical-type group was significantly higher than common type (p < 0.001). The ICC value of the two observers was 0.976 (95% CI 0.962-0.985). ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918. The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. CONCLUSIONS: The proportion of clinical mild-type patients with COVID-19 was relatively high; CT was not suitable for independent screening tool. The CT visual quantitative analysis has high consistency and can reflect the clinical classification of COVID-19. KEY POINTS: • CT visual quantitative evaluation has high consistency (ICC value of 0.976) among the observers. The median TSS of severe-critical type group was significantly higher than common type (p < 0.001). • ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918 (95% CI 0.843-0.994). The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. • The proportion of confirmed COVID-19 patients with normal chest CT was relatively high (30.8%); CT was not a suitable screening modality.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , Aged , Aged, 80 and over , Area Under Curve , COVID-19 , Female , Humans , Male , Middle Aged , Pandemics , ROC Curve , Retrospective Studies , SARS-CoV-2 , Thorax , Tomography, X-Ray Computed/methods , Vision, Ocular
6.
Radiology ; 295(1): 202-207, 2020 04.
Article in English | MEDLINE | ID: covidwho-333

ABSTRACT

In this retrospective case series, chest CT scans of 21 symptomatic patients from China infected with the 2019 novel coronavirus (2019-nCoV) were reviewed, with emphasis on identifying and characterizing the most common findings. Typical CT findings included bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. Notably, lung cavitation, discrete pulmonary nodules, pleural effusions, and lymphadenopathy were absent. Follow-up imaging in a subset of patients during the study time window often demonstrated mild or moderate progression of disease, as manifested by increasing extent and density of lung opacities.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Adult , Aged , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Coronavirus Infections/complications , Coronavirus Infections/diagnosis , Coronavirus Infections/pathology , Disease Progression , Female , Humans , Lung/pathology , Male , Middle Aged , Pneumonia, Viral/complications , Pneumonia, Viral/pathology , Retrospective Studies , SARS-CoV-2 , Severe Acute Respiratory Syndrome/diagnostic imaging
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