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AI functions in x-ray systems may save radiographers time

AuntMinnie

Intelligent virtual and AI-based collimation features appear to save radiographers time during x-ray image acquisitions – a key function for enabling more patient-focused workflows, according to a recent study. To that end, the researchers conducted an observational study at five clinical sites in Europe and the U.S.

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Julianna Czum talks AI chest radiograph study with AuntMinnie.com

AuntMinnie

million chest radiographs. The team reported that the algorithm could successfully triage pairs of chest radiographs showing no change while detecting urgent interval changes during longitudinal follow-up. Julianna Czum, MD, from Johns Hopkins University wrote an editorial accompanying the study.

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Injury Prediction Rule Reduces Radiographic Imaging Exposure in Children

MedImaging Radiography

A highly accurate prediction rule for cervical spine injuries in children reduces the use of CT scans by over 50% without missing clinically significant injuries or increasing the use of unnecessary X-rays.

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Can We Upskill Radiographers through Artificial Intelligence? 

The British Institute of Radiology

Shamie Kumar describes how AI fits into a radiology clinical workflow and her perspective on how a clinical radiographer could use this to learn from and enhance their skills. If the AI findings are seen in PACS, how many radiographers actually log into PACS after taking a scan or X-ray? Can Radiographers Up-Skill?

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Deep-Learning Chest Radiograph Model Predicts Mortality for Community-Acquired Pneumonia

Imaging Technology

The deep learning (DL) model may guide clinical decision-making in the management of patients with CAP by identifying high-risk patients who warrant hospitalization and intensive treatment,” concluded first author Eui Jin Hwang, MD, PhD, from the department of radiology at Seoul National University College of Medicine in Korea. Hwang et al.

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Deep learning–based identification of spine growth potential on EOS radiographs

European Society of Radiology: AI

In this study, the authors developed a deep learning-based algorithm, which is able to mimic human judgment, in order to help clinicians assess the potential of spine growth based on EOS radiographs. Key points In the clinic, there is no available computer-based method that can automatically assess spine growth potential.

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CMCU Digital Radiography (DR) Group Introduces Clinical White Paper on Intelligent Noise Reduction in Pediatric Radiography Study

Imaging Technology

The company will showcase the clinical analysis of Canon’s Intelligent Noise Reduction (Intelligent NR) that provides superior image quality while lowering radiation dosing in pediatric digital radiography at the Radiological Society of North America Annual Meeting 2023 (RSNA) , McCormick Place Convention Center, Chicago, IL Nov.