RSNA to publish 3 new online journals

AuntMinnie.com staff writers

Oct 17, 2017

The RSNA will begin publishing three new subspecialty online journals in 2019 on cancer imaging, cardiothoracic imaging, and machine learning/artificial intelligence (AI).

Each journal will feature both original research and topical reviews, according to the RSNA. The journals will complement Radiology and RadioGraphics and provide a resource for practicing physicians and imaging researchers to stay up-to-date on emerging sciences in each subspecialty.

New submissions to the online publications will be accepted in 2018, and the search for editors will begin next month. RSNA members will receive access to all of these journals as a benefit of membership.

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Researchers explain the data preparation and structure of a proposed CNN-based deep learning model using mammography features to determine breast tumor staging. The image is available for use under a Creative Commons license: CC BY-NC-ND 4.0 DEED Attribution-NonCommercial-NoDerivs 4.0 International.

Deep-learning model determines breast tumor staging

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16 Bit gets FDA nod for AI x-ray software

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Blackford and Lucida Medical partner on prostate cancer imaging

By AuntMinnie.com staff writers

Blackford has entered a commercial partnership with Lucida Medical to integrate Lucida's prostate cancer MRI imaging software (Pi) into its AI platform.

April 17, 2024

CT-based deep learning identifies patients at further lung cancer risk

By Kate Madden Yee

A CT-based deep learning model can identify individuals with clinical stage IA non-small cell lung cancer (NSCLC) at high risk of further disease, researchers have found.

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Deep-learning model determines breast tumor staging

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A deep-learning model based on mammographic images can determine tumor staging and sentinel lymph node metastasis in breast cancer patients.

April 16, 2024

Researchers explain the data preparation and structure of a proposed CNN-based deep learning model using mammography features to determine breast tumor staging. The image is available for use under a Creative Commons license: CC BY-NC-ND 4.0 DEED Attribution-NonCommercial-NoDerivs 4.0 International.

16 Bit gets FDA nod for AI x-ray software

By AuntMinnie.com staff writers

Canadian AI software developer 16 Bit has received de novo approval from the U.S. Food and Drug Administration (FDA) for Rho, an AI tool for evaluating low bone mineral density.

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Vision transformer AI model boosts PET/CT imaging of cancer

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Predictions and Grad-CAMs of ViT-based models on sample PET/CT, PET, and CT test images from the “positive” class. ViT was fine-tuned using training data for each modality. The bounding boxes depicted in the figure indicate malignant lesions. The top row of the Grad-CAMs shows important areas for “positive” predictions, and the bottom row shows areas for “negative” predictions. Image courtesy of Scientific Reports.

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Jennifer Bagley from the University of Oklahoma presents findings at UltraCon on the performance of two machine learning approaches in predicting burnout among sonographers.

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An AI-based method can outperform breast density measurements for flagging patients who would benefit from supplemental breast cancer screening.

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Breast Cancer 3 D

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ChatGPT powered by GPT-4 and using DALL-E−3 was asked to generate an image for teaching purposes that demonstrated: a nuclear medicine bone scan with skeletal metastases (top left); coronary artery disease (top right); an F-18 FDG-PET scan of the brain with Alzheimer's disease (bottom left); and an x-ray showing a fracture (bottom right). These are misleading for educational purposes and for patients who may be searching for information. Image and caption courtesy of Radiography.

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Chest Lung Xray 400

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A visual abstract of the study by the authors. Image courtesy of the Journal of Nuclear Medicine.

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