A Novel Approach for Detecting Abnormality in Ejection Fraction Using Transthoracic Echocardiography with Deep Learning

Cardiovascular diseases (CVD) are the prime cause of mortality in people worldwide. Mortality in CVD has been strongly linked to Ejection Fraction (EF) in various studies1. Left ventricular ejection fraction (LVEF) is the central measure of left ventricular systolic function. LVEF is the fraction of chamber volume ejected in systole (stroke volume) in relation to the volume of the blood in the ventricle at the end of diastole (end-diastolic volume)2. Evaluation of left ventricular systolic function by left ventricle ejection fraction (EF) using Transthoracic echocardiography is usually a first line investigation. Determination of Ejection fraction (EF) is done most commonly by a semi-automatic process in which echocardiographer segments the left ventricle in both systolic and diastolic frames to generate systolic and diastolic chamber dimensions. The whole process in time consuming and highly dependent on operator experience causing a lot of inter-observer and intra-observer variations. Our goal is to develop algorithms so as to reduce the time consumed during whole process and make it more reliable and reproducible. We have used M-Mode of Left ventricle in PLAX view to measure chamber dimensions and calculate EF by Teich method. EF &gt;50% has been categorized as normal ejection fraction. EF &lt; 50% has been categorized as reduced ejection fraction and LV systolic dysfunction. In this research we have used fine-tuned ResNet 50 and trained it with 200 cases. We observed an accuracy of 98% and a F1 score of 77% for reduced EF (&lt;50%) and 77% for normal EF (&gt;50%). Although this is a small dataset, it shows that deep learning algorithms can be applied to medical imaging. ResNet50 is a preferred choice in terms of accuracy. This research will serve as a stepping stone for future research and will determine other cardiac matrices.</p

Tumor necrosis factor-alpha −308G/A gene polymorphism and novel biomarker profiles in patients with Takayasu arteritis

Background: Takayasu arteritis (TA) is an idiopathic chronic inflammatory disease of the aorta and its branches, leading to stenosis, occlusion, and aneurysmal dilatation. Tumor necrosis factor-alpha (TNF-α) is a cytokine with pleomorphic actions and plays a pivotal role in inflammation; the serum level of TNF-α is genetically determined. However, the literature lacks adequate information on the association of TNF-α polymorphisms with TA. Hence, the present study investigates the contribution of TNF-α polymorphism toward the complex etiology of TA. Methods: A cross-sectional study was performed in 87 patients with TA and 90 controls. A promoter region polymorphism of TNF-α, rs1800629 G/A, or −308G/A was genotyped in all the study subjects followed by a case–control association study. Furthermore, to understand the biomarker profile, levels of specific markers such as erythrocyte sedimentation rate, serum high-sensitivity C-reactive protein, interleukin-18, interleukin-6, and TNF-α were measured in all the study subjects. Results: All the inflammatory markers were significantly higher in the TA patients than in the controls. The genetic study (available for 57 TA patients and 36 controls) revealed that the TNF-α −308A allele was overrepresented in the TA patients (12% vs 7%). The TNF-α −308A allele correlated with the increased TNF-α levels, but it could not attain significance because of a small sample size. Conclusion: The TNF-α −308G/A polymorphism is associated with TNF-α levels in Indian population, which might have implications for clinical risk stratification and treatment. The different TNF-α gene promoter polymorphism might contribute to the molecular pathogenesis of TA. However, further study of the underlying mechanism is warranted. Keywords: Takayasu arteritis, Biomarkers, Tumor necrosis factor-alpha, Gene polymorphis