This is a ongoing study done in National Institute of Cardiovascular Diseases (NICVD), Dhaka, Bangladesh during the period May 2002 to June 2003. Total number of study population was 150 with male 135 & female 15. Indication of percutaneous coronary intervention (PCI) was Unstable angina with no prior myocardial infarction 60 cases. Myocardial infarction (MI) inferior with postmyocardial infarction angina 40 cases & MI anterior with post MI angina 50 cases. The risk factors were Hypertension (HTN) in 72 cases (48%), smoking in 90 cases (60%), Diabetes Mellitus (DM) in 50 cases (33.33%), Positive family history for ischemic heart disease (IHD) in 40 cases (26.66%) and Dyslipidemia 35 cases (23.32%), Total number of target coronary arteries were 190 of which 184 lesions treated. Distribution of 184 lesions were left anterior descending coronary artery (LAD) 91 (Proximal LAD 40, mid LAD 40, distal LAD 11), Right coronary artery (RCA) 60 (Proximal 15, mid RCA 40, distal RCA 05), Left circumflex coronary artery (LCX) 26 (Proximal LCX 05, mid LCX 15, distal LOX 04, LCX PD 02), Diagonal 02,Obtuse marginal (OM) 04, Ramus intermedius 02.
Introduction: Exercise tolerance test (ETT) is an established screening test for coronary artery disease (CAD), but not feasible in 30−40% of patients. Dobutamine stress echocardiography (DSE) is an excellent alternative. Traditionally, inducible worsening of wall motion by 1 grade from baseline provides an index of CAD; worsening by 2 grade or more theoretically represents a more severe perfusion abnormality. The present study represents the inaugural experience of DSE at the National Institute of Cardiovascular Disease, Dhaka. Objective: To assess the predictive accuracy of DSE results with the presence and extent of CAD in subjects with suspected stable angina pectoris. Materials and Methods: In this prospective observational study, 35 subjects with intermediate to high probability of CAD were subjected to DSE followed by coronary angiography (CAG) within one month. Comparison of DSE results and predicted coronary artery involvement with angiographic findings were done. Overall sensitivity, specificity, accuracy as well as accuracy by arterial territory involvement were calculated. Results: DSE identified 82 abnormal segments, 66 with 1 grade change in 23 subjects (Group A) and 16 with 2 grade change in 8 subjects (Group B). CAG detected 54 significant lesions, 23 (42.59%) in left circumflex (LCX), 18 (33.33%) in left anterior descending (LAD), 11 (20.37%) in right coronary (RCA) and 2 (3.7%) in left coronary (LCA) artery. DSE had a sensitivity of 93.1% and a specificity of 66.7%. The accuracy was 88.57% overall, 94.29% for LAD and 91.43% for both LCX and RCA territories. Group B subjects had significantly higher number of coronary stenosis per patient (2.63 versus 1.38, p<0.001), triple vessel (62.5% versus 8.6%, p=0.003) and lower single vessel CAD (0% versus 47.8%, p=0.005). Conclusion: This study shows that DSE is a reliable test for prediction of the presence and extent of CAD. J Enam Med Col 2019; 9(1): 16-24
To correlate magnitude and distribution of coronary Artery Disease (CAD) with ischaemic limb changes in patients with Peripheral Arterial Disease (PAD) of lower limbs.Most common cause of PAD is atherosclerosis. Atherosclerosis is a generalized disease and often atherosclerotic CAD is associated with PAD. Because of ischaemic limb changes in patient with PAD they do not always experience angina even after having CAD. This prospective observational study was conducted in National Institute of cardiovascular Diseases (NICVD) Dhaka, Bangladesh during July 2004 to June 2005. Total 58 patients with PAD were included in the study. Patients were classified as group I having normal coronary artery, group-II insignificant CAD (L M <50% stenosis, others <70% stenosis) and group-III significant CAD (LMe 50% stenosis, others e 70%, stenosis). Ischaemic limb changes was significantly higher in patients with CAD compare to non-coronary artery disease (P = 0.047).This study suggests that ischaemic limb changes had significant relation with the presence of CAD. DOI: http://dx.doi.org/10.3329/uhj.v8i1.11660 University Heart Journal Vol. 8, No. 1, January 2012
<p>The purpose of our study was to evaluate Microsoft Cognitive Service to detect COVID19 induced pneumonia and ordinary viral or bacterial infection in Lung using X-Ray and CT scan images. We have used Datasets from a recognized and trusted source to build our model. The primary objective is a Smartphone based on device real-time inference system. In this case, the model would run by a mobile device’s System on Chip (SoC) and will not require an internet connection for inference with zero latency. This system would be particularly suitable for rural areas of developing countries where internet connection is poor or not available. The secondary solution would be a web portal running the inference through REST API from Custom Vision.</p><p> </p><p>Now, given the nature of The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), which causes respiratory disease as a novel one, the majority of the radiologists are not acquainted enough to detect the virus-related changes from the X-Ray. Moreover, the morphology of COVID-19 and common Pneumonia are hard to differentiate from X-Ray alone without the patient's symptoms by a radiologist.</p><p> </p><p> </p><p>Here, AI comes into play with the role of an expert assistant. It is much faster and efficient to train a machine over thousands of labeled training data to observe and detect subtle differences between various X-Ray images to train its Artificial Neural Network and classify them quickly which is otherwise not possible by a human eye. A Radiologist can use the app to primarily identify the X-Ray in question and combine it with his/her medical expertise along with the patient's case history before in conjunction with tests like RT PCR/Antibody. </p>
AI is leveraging all aspects of life. Medical services are not untouched. Especially in the field of medical image processing and diagnosis. Big IT and Biotechnology companies are investing millions of dollars in medical and AI research. The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. Lung disease remains a major healthcare challenge with high morbidity and mortality worldwide. The predominant lung disease was lung cancer. Until recently, the world has witnessed the global pandemic of COVID19, the Novel coronavirus outbreak. We have experienced how viral infection of lung and heart claimed thousands of lives worldwide. With the unprecedented advancement of Artificial Intelligence in recent years, Machine learning can be used to easily detect and classify medical imagery. It is much faster and most of the time more accurate than human radiologists. Once implemented, it is more cost-effective and time-saving. In our study, we evaluated the efficacy of Microsoft Cognitive Service to detect and classify COVID19 induced pneumonia from other Viral/Bacterial pneumonia based on X-Ray and CT images. We wanted to assess the implication and accuracy of the Automated ML-based Rapid Application Development (RAD) environment in the field of Medical Image diagnosis. This study will better equip us to respond with an ML-based diagnostic Decision Support System(DSS) for a Pandemic situation like COVID19. After optimization, the trained network achieved 96.8% Average Precision which was implemented as a Web Application for consumption. However, the same trained network did not perform like Web Application when ported to Smartphone for Real-time inference, which was our main interest of study. The authors believe, there is scope for further study on this issue. One of the main goals of this study was to develop and evaluate the performance of AI-powered Smartphone-based Real-time Applications. Facilitating primary diagnostic services in less equipped and understaffed rural healthcare centers of the world with unreliable internet service.
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