AI Medical Compendium Topic:
Radiology

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Radiology and Enterprise Medical Imaging Extensions (REMIX).

Journal of digital imaging
Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and clinical research operational needs of Department of Radiology of The Ohio State University Wexner M...

Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes.

Journal of digital imaging
Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and clinical research. Harnessing the full potential of the EMR requires scalable approaches such as nat...

AI and Clinicians: Not a Mutually Exclusive Zero-Sum Game.

IEEE pulse
Recent bold, eye-catching headline predictions made by nonradiologists, e.g., "in a few years, radiology will disappear" and "stop training radiologists now," are not only far from reality but also irresponsible and a disservice to the appropriate im...

When Machines Think: Radiology's Next Frontier.

Radiology
Artificial intelligence (AI), machine learning, and deep learning are terms now seen frequently, all of which refer to computer algorithms that change as they are exposed to more data. Many of these algorithms are surprisingly good at recognizing obj...

Redefining the Practice of Peer Review Through Intelligent Automation Part 2: Data-Driven Peer Review Selection and Assignment.

Journal of digital imaging
In conventional radiology peer review practice, a small number of exams (routinely 5% of the total volume) is randomly selected, which may significantly underestimate the true error rate within a given radiology practice. An alternative and preferabl...

The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children.

Journal of thrombosis and thrombolysis
Venous thromboembolism (VTE) is a potentially life-threatening condition that includes both deep vein thrombosis (DVT) and pulmonary embolism. We sought to improve detection and reporting of children with a new diagnosis of VTE by applying natural la...

Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing.

Journal of digital imaging
We built a natural language processing (NLP) method to automatically extract clinical findings in radiology reports and characterize their level of change and significance according to a radiology-specific information model. We utilized a combination...

Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images.

Journal of digital imaging
The purpose of this study is to evaluate transfer learning with deep convolutional neural networks for the classification of abdominal ultrasound images. Grayscale images from 185 consecutive clinical abdominal ultrasound studies were categorized int...