Since 2002, transcatheter aortic valve implantation (TAVI) has revolutionized the treatment and prognosis of patients with aortic stenosis. A preprocedural assessment of the patient is vital for achieving optimal outcomes from the procedure. Retrospective ECG-gated cardiac computed tomography (CT) today it is the gold-standard imaging technique that provides three-dimensional images of the heart, thus allowing a rapid and complete evaluation of the morphology of the valve, ascending aorta, coronary arteries, peripheral access vessels, and prognostic factors, and also provides preprocedural coplanar fluoroscopic angle prediction to obtain complete assessment of the patient. The most relevant dimension in preprocedural planning of TAVI is the aortic annulus, which can determine the choice of prosthesis size. CT is also essential to identify patients with increased anatomical risk for coronary artery occlusion in Valve in Valve (ViV) procedures.
Moreover, CT is very useful in the evaluation of late complications, such as leakage, thrombosis and displacements. At present, CT is the cornerstone imaging modality for the extensive and thorough work-up required for planning and performing each TAVI procedure, to achieve optimal outcomes. Both the CT procedure and analysis should be performed by trained and experienced personnel, with a radiological background and a deep understanding of the TAVI procedure, in close collaboration with the implantation team. An accurate pre-TAVI CT and post-processing for the evaluation of all the points recommended in this review allow a complete planning for the choice of the valve dimensions and type (balloon or self-expandable) and of the best percutaneous access.
Objectives: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in early phase of the disease. Recently Artificial Intelligence systems for the diagnosis of Covid-19 related pneumonia on Chest X ray (CXR) or chest CT have been tested with variable, but not negligible, accuracy. Texture analysis might be an additional tool for the evaluation of CXR in patients with clinical suspicion of Covid-19 related pneumonia.Methods: CXR images were accessed from a publicly available repository (https://www.kaggle.com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal regions of interest (ROI) covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 CXR images were selected for the final analysisResults: Six models, namely NB, GLM, DL, GBT, ANN and PLS-DA were selected and ensembled. According to Youden’s index, the Covid-19 Ensemble Machine Learning (EML)-Score showing the highest AUCROC (0.971±0.015) was 132.57. Assuming this cut-off the EML model performance was estimated evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the EML showed 100% sensitivity, with 80% specificityConclusion: Texture analysis of CXR images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay ground for future researches in this field and help developing more rapid and accurate screening tools for these patients.
Our study remarks the importance of CT assessment in the overall management and diagnostic framework of TAVI candidates; the information provided is essential in order to minimize possible complications and to improve the quality of the therapeutic planning.
Background:
One of the most challenging aspects related to Covid-19 is to establish the presence of infection in early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumonia.
Objective:
To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images.
Methods:
Chest X-ray images were accessed from a publicly available repository (https://www.kaggle.com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal regions of interest covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 Chest X-ray images were selected for the final analysis.
Results:
Six models, namely NB, GLM, DL, GBT, ANN and PLS-DA were selected and ensembled. According to Youden’s index, the Covid-19 Ensemble Machine Learning Score showing the highest Area Under the Curve (0.971±0.015) was 132.57. Assuming this cut-off the Ensemble model performance was estimated evaluating both true
and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the Ensemble Machine Learning showed 100% sensitivity, with 80% specificity.
Conclusion:
Texture analysis of Chest X-ray images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay ground for future researches in this field and help developing more rapid and accurate screening tools for these patients.
Soft-tissue hematomas are a common clinical entity often associated with trauma, surgery, and bleeding disorders. In the majority of cases, soft-tissue hematomas acutely appear and spontaneously resolve, but sometimes, they present as swellings that slowly expand and progressively increase with time.We present a case of a 70-year-old man with chronic expanding hematoma of the left flank without any history of recent trauma or other medical disease.The diagnosis could not be confirmed on imaging features alone, so the patient was taken to surgery for open biopsy and excision.In patients with slowly growing extremity masses without recent trauma or chronic medical disorders, the differential diagnosis becomes challenging, and chronic expanding hematoma should be considered in addition to soft-tissue sarcomas and other malignancies.
Background
To present a case of anomalous origin of the left coronary artery evaluated with invasive coronary angiography (ICA) and ECG-gated coronary computed tomography (CCT).
Case presentation
A patient (55 years old, male) with a past medical history of respiratory failure and atrial fibrillation underwent ICA to rule out coronary artery disease. Subsequently, the patient underwent ECG-gated CCT to evaluate a suspected anomalous aortic origin of the left coronary artery, since the interventional cardiologist was not able to properly identify the left coronary artery and its distal branches. CCT showed left coronary artery originating from the right coronary Valsalva sinus, coursing within the interventricular septum and emerging at the middle segment of the interventricular sulcus, where the left anterior descending and circumflex arteries originated.
Conclusion
The case we presented highlights the value of ECG-gated CCT in the evaluation of coronary anomaly anatomy and thus risk stratification derived by proper coronary anatomy assessment. Although ICA was not helpful in the diagnosis, it also has a pivotal role regarding the therapeutic management of this condition.
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