Research Publications

Role of multidetector CT (64 Slice) in the evaluation of living potential donors for renal transplantation

Abstract Background:Renal transplantation is the definitive therapy for the end stage renal disease. This study was undertaken to assess the role of MDCT (64-slice) in the preoperative evaluation of living potential renal transplantation donors for transplantation. Methods: This prospective one year study included 31 potential living renal donors aged between 22-69 years, sent to the department […]

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Utility of contemporaneous dual read in the setting of emergency teleradiology reporting

Abstract Purpose: Emergency radiology requires rapid and accurate interpretation of imaging examinations. Missed findings may lead to adverse outcomes. Double reporting may be used to minimize errors. Limited contemporaneous double reporting may be most efficient and cost-effective, but no data exists. This study is intended to examine the benefits of double reading and identify examinations where

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Multi-detector CT in the pre-operative assessment of live donors for liver transplantation

Abstract Background: Transplantation surgeons are now performing living donor liver transplantation because of the severe shortage of cadaveric livers. This study was undertaken to assess the role of 64-slice multidetector CT in the pre op evaluation of living related liver donors for transplantation. Methods: This was a prospective correlation study of 26 patients who were referred to

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MDCT appearance of Organoaxial, Mesenteroaxial Types of Gastric Volvulus

Abstract Introduction: Gastric volvulus is a rare cause of acute or recurring abdominal pain in adults, with significantmortality and morbidity. Gastric volvulus is rotation of stomach along its axis, causing gastric outlet obstructionand can result in ischaemia and infarction. Aim: Evaluation of differentiating findings of organoaxial, and mesenteroaxial types of gastric volvulus on Multidetector Computed

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Evaluating patients with pulmonary thromboembolism: a retrospective study on the utilization of serum D-dimer assay and CT angiography

Abstract Background and objective: Serum D-dimer assay is an inexpensive laboratory test with a high negative predictive value, obviating the need for computed tomographic pulmonary angiography (CTPA) in patients with low clinical probability of thromboembolic disease. It has been recommended that CTPA may be performed only in patients with positive D-dimer assays and clinical suspicion of PTE (pulmonary

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Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays

Abstract The coronavirus disease of 2019 (COVID-19) pandemic exposed a limitation of artificial intelligence (AI) based medical image interpretation systems. Early in the pandemic, when need was greatest, the absence of sufficient training data prevented effective deep learning (DL) solutions. Even now, there is a need for Chest-X-ray (CxR) screening tools in low and middle

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Interobserver Variation in Response Evaluation Criteria in Solid Tumors

Abstract Purpose: Response Evaluation Criteria in Solid Tumors (RECIST 1.1) is the gold standard for imaging response evaluation in cancer trials. We sought to evaluate consistency of applying RECIST 1.1 between 2 conventionally trained radiologists, designated as A and B; identify reasons for variation; and reconcile these differences for future studies. Methods: The study was approved as

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Pneumothorax Detection and Classification on Chest Radiographs using Artificial Intelligence

Abstract In recent years, Computer Aided Diagnosis (CAD) systems have been designed for the detection of lung space anomalies.Pneumothorax is an abnormal collection of air in the pleural space between the lung and the chest wall that can result inpartial or complete lung collapse [1]. This is a medical emergency in which quick detection and

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Pneumonia Detection and Classification on Chest Radiographs using Deep Learning

Abstract Computer Aided Diagnosis (CAD) is progressively becoming a reliable tool in enhancing the productivity and accuracy of a radiologist in detecting abnormalities on chest radiographs. Detection of airspace disease such as pneumonia can be facilitated with the help of image processing and deep learning algorithms. In this study, we aim to develop and evaluate

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