Detecting COVID-19 in chest X-ray images

One reliable way of detecting coronavirus disease 2019 (COVID-19) is using a chest x-ray image due to its complications in the lung parenchyma. This paper proposes a solution for COVID-19 detection in chest x-ray images based on a convolutional neural network (CNN). This CNN-based solution is developed using a modified InceptionV3 as a backbone architecture. Self-attention layers are inserted to modify the backbone such that the number of trainable parameters is reduced and meaningful areas of COVID-19 in chest x-ray images are focused on a training process. The proposed CNN architecture is then learned to construct a model to classify COVID-19 cases from non-COVID-19 cases. It achieves sensitivity, specificity, and accuracy values of 93%, 96%, and 96%, respectively. The model is also further validated on the so-called other normal and abnormal, which are non-COVID-19 cases. Cases of other normal contain chest x-ray images of elderly patients with minimal fibrosis and spondylosis of the spine, whereas other abnormal cases contain chest x-ray images of tuberculosis, pneumonia, and pulmonary edema. The proposed solution could correctly classify them as non-COVID-19 with 92% accuracy. This is a practical scenario where non-COVID-19 cases could cover more than just a normal condition

Prevalence and prognosis of myocardial scar in patients with known or suspected coronary artery disease and normal wall motion

<p>Abstract</p> <p>Background</p> <p>Some patients may have normal wall motion after myocardial infarction. The aim of this study was to determine the prevalence and prognosis of patients with myocardial scar in the absence of abnormal wall motion. We studied patients with suspected or known coronary artery disease (CAD) who were referred for cardiovascular magnetic resonance (CMR) for the assessment of global and regional cardiac function and late gadolinium enhancement (LGE) and had normal left ventricular wall motion. Prognostic value was determined by the occurrence of hard endpoints (cardiac death and nonfatal myocardial infarction) and major adverse cardiac events (MACE) which also included hospitalization due to unstable angina or heart failure or life threatening ventricular arrhythmia.</p> <p>Results</p> <p>A total 1148 patients (70.3%) were studied. LGE was detected in 104 patients (9.1%). Prevalence of LGE increased in patients with increased left ventricular mass. Average follow-up time was 955 ± 542 days. LGE was the strongest predictor for hard endpoints and MACE.</p> <p>Conclusion</p> <p>LGE was detected in 9.1% of patients with suspected or known CAD and normal wall motion. LGE was the strongest predictor of significant cardiac events.</p

Diffusion-weighted magnetic resonance imaging for the assessment of liver fibrosis in chronic viral hepatitis.

BackgroundAccurate noninvasive methods for the assessment of liver fibrosis are urgently needed. This prospective study evaluated the diagnostic accuracy of diffusion-weighted magnetic resonance imaging (DWI) for the staging of liver fibrosis and proposed a diagnostic algorithm using DWI to identify cirrhosis in patients with chronic viral hepatitis.MethodsOne hundred twenty-one treatment-naïve patients with chronic hepatitis B or C were evaluated with DWI followed by liver biopsy on the same day. Breath-hold single-shot echo-planar DWI was performed to measure the apparent diffusion coefficient (ADC) of the liver and spleen. Normalized liver ADC was calculated as the ratio of liver ADC to spleen ADC.ResultsThere was an inverse correlation between fibrosis stage and normalized liver ADC (p3.25 yielded an 80% PPV for cirrhosis, and a 100% NPV to exclude cirrhosis in patients with Fibrosis-4 between 1.45 and 3.25. Only 15.7% of patients would require a liver biopsy. This sequential strategy can reduce DWI examinations by 53.7%.ConclusionNormalized liver ADC measurement on DWI is an accurate and noninvasive tool for the diagnosis of cirrhosis in patients with chronic viral hepatitis

Comparison of cardiovascular magnetic resonance of late gadolinium enhancement and diastolic wall thickness to predict recovery of left ventricular function after coronary artery bypass surgery

Abstract Background The objective was to compare the value of late gadolinium enhancement (LGE) and end-diastolic wall thickness (EDWT) assessed by cardiovascular magnetic resonance (CMR) in predicting recovery of left ventricular function after coronary artery bypass surgery (CABG). Methods We enrolled patients with coronary artery disease and left ventricular ejection fraction Results We studied 46 men and 4 women with an average age of 61 years. Baseline left ventricular ejection fraction was 37 ± 13%. A total of 2,020 myocardial segments were analyzed. Abnormal wall motion and the LGE area were detected in 1,446 segments (71.6%) and 1,196 segments (59.2%) respectively. Wall motion improvement was demonstrated in 481 of 1,227 segments (39.2%) that initially had wall motion abnormalities at baseline. Logistic regression analysis showed that the LGE area, EDWT and resting wall motion grade predicted wall motion improvement. Comparison of Receiver-Operator-Characteristic (ROC) curves demonstrated that the LGE area was the most important predictor (p Conclusion LGE and EDWT are independent predictors for functional recovery after revascularization. However, LGE appears to be a more important factor and independent of EDWT.</p

Intersite validations of the pixel-wise method for liver R2* analysis in transfusion-dependent thalassemia patients: a more accessible and affordable diagnostic technology

BACKGROUND AND OBJECTIVES: MRI-R2* has been accepted as a clinical tool for monitoring iron overload in thalassemia patients, especially for monitoring liver iron concentration (LIC). the most optimal and practical method of analysis however, is still open to further investigations. our objective was to investigate intra- and intersite observer variability of the pixel-wise method for liver R2* analysis in thalassemia patients using a mono-exponential with a constant offset model. PATIENTS AND METHODS: We performed 88 liver R2* measurements on 72 thalassemia major patients. a single breath-hold multi-echo gradient-echo sequence was acquired and analyzed at both the reference (REF) and local (LoC) sites. the analysis defined the region of interest in the whole liver parenchyma, excluding the great vessels, and were reported as median values. RESULTS: The R2* values from the REF and LOC were statistically comparable for all comparisons. the intrasite and intersite observer variation were 0.75% (less than 0.9%) and 2.5%, respectively, both of which are comparable to previous reports, but substantially lower than conventional region-based approaches. CONCLUSION: The low variation of the R2* also yielded excellent variation in the tabulated hepatic iron content. however, caution is required when comparing the results to different implementation methods and appropriate evaluation and validation of methodology for any new scan site is essential before its clinical use

Classification of chest radiography from general radiography using deep learning approach

Classifying x-ray images into individual classes of body parts is needed, when they are mixed without proper labels. This paper proposes a hierarchical training of convolutional neural network (CNN)-based framework, for classifying chest posterior–anterior (PA) x-ray images from other 12 classes. The first model is constructed for filtering chest PA from the other classes, before constructing the second model to separate the rest of the 12 classes. This is beneficial to address class-imbalanced and overfitting problems, with assists of class weighting and data augmentation. The proposed method achieves promising performances with precision and recall of 100% and F0.5of 99%