Arjun K.

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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2b or not 2b

While on the subject of quality assurance and peer review, one observation I have is that interobserver variability is a real thing that critically affects the practice of radiology. Nowhere is this more impactful than in peer review and QA. While teleradiology practices have raised the bar across the radiology community given the higher degree

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7 signs that diagnostic centers should invest in teleradiology

In today’s competitive healthcare environment, running a diagnostic imaging center is not easy. Apart from the high equipment cost at startup, operational and ongoing financial challenges abound. A technology innovation that can assist owners of diagnostic centers in optimizing their center’s performance is teleradiology. This article lists the typical scenarios in which owners of diagnostic

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Benefits of Teleradiology

Telemedicine is the application of information technology and telecommunications networks for the purpose of medical diagnosis and therapy from remote locations. A host of recent technology innovations have made it possible for telemedicine to expand its reach across every medical speciality– its usage in radiology is called “Teleradiology.” Radiology incorporates the diverse methods used in medical science

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