Live from HIMSS 2019: The PACSman on AI and radiology

Feb 13, 2019

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ORLANDO, FL - What needs to happen next before artificial intelligence (AI) can become a routine clinical tool in radiology? We talked to the PACSman, aka Michael J. Cannavo, about the intersection of AI and radiology at this week's Healthcare Information and Management Systems Society (HIMSS) meeting.

Michael J. Cannavo talks about the intersection of AI and radiology.

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

April 16, 2024

16 Bit gets FDA nod for AI x-ray software

April 12, 2024

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.

Vision transformer AI model boosts PET/CT imaging of cancer

April 12, 2024

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

UltraCon: Machine learning predicts sonographer burnout

April 9, 2024

Deep-learning model determines breast tumor staging

By Amerigo Allegretto

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.

April 12, 2024

Vision transformer AI model boosts PET/CT imaging of cancer

A vision transformer AI model outperformed convolutional neural networks when classifying tumors on PET/CT images as benign or malignant.

April 12, 2024

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.

UltraCon: Machine learning predicts sonographer burnout

By Amerigo Allegretto

Machine-learning approaches can predict burnout among sonographers, though established burnout indices may be enough, according to research presented April 9 at UltraCon.

April 9, 2024

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

AI helps select patients for supplemental breast cancer screening

By Erik L. Ridley

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

April 9, 2024

Breast Cancer 3 D

AI model predicts invasiveness of lung cancer on CT scans

Radiologists in Beijing, China, have developed a joint deep-learning and radiomics AI model that can flag how invasive tumors may be in patients with lung cancer.

April 9, 2024

Can AI improve sustainability in nuclear medicine?

Nuclear medicine experts support the use of AI to improve sustainability of practices in the field, yet say implementation will require careful consideration of the technology’s benefits and detriments.

April 9, 2024

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.

Northwestern, Dell develop LLM for chest x-ray interpretation

By AuntMinnie.com staff writers

Dell Technologies and Northwestern Medicine are collaborating on a generative multimodal large language model (LLM) for the interpretation of chest x-rays.

April 5, 2024

Chest Lung Xray 400

SPECT/CT algorithm predicts heart failure hospitalizations

AI can effectively predict hospitalization for acute heart failure exacerbation in patients undergoing SPECT/CT myocardial perfusion imaging.

April 3, 2024

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

ChatGPT performs well on radiation oncology patient care questions

ChatGPT may be a valuable resource for radiation oncology patients.

April 2, 2024

Can AI help chest DDR achieve clinical adoption?

Chest dynamic digital radiography (DDR) may have received a boost toward clinical use in patients with lung disorders, with researchers developing AI to perform time-consuming analysis involved in the technology.

April 1, 2024

(a) Raw example of a dynamic digital radiograph. (b) Contrast equalization using contrast-limited adaptive equalization shows improved anatomical visualization. (c) Training data is generated through manually labeling using SLEAP’s graphical user interface. (d-e) Neural network detection schematic. For each quadrant a series of two networks were used. The first identifies the quadrant center, the second using input of the first, then identifies individual anatomical landmarks within a quadrant. (d) Four neural networks were trained, each with the objective of identifying a chest quadrant centroid. The four separate quadrants are visualized as colored boxes (right upper quadrant, green; right lower quadrant, blue; left upper quadrant, red; left lower quadrant, yellow). (e) Serial centroid then anatomical identification. Two quadrant examples showing confidence maps of trained neural networks identifying quadrant centroids (left) and then anatomical confidence maps (right). Image and caption courtesy of Chest Pulmonary.

Deep learning helps distinguish benign vertebral fractures on CT

By Kate Madden Yee

A deep-learning algorithm used with CT imaging can differentiate between benign and malignant vertebral fractures, researchers have found.

March 27, 2024

Overview of the automated deep learning-based pipeline. Multidetector CT images show the (A) original data, (B, C) vertebral segmentation, and (D-F) vertebral labeling during preprocessing. Images and caption courtesy of the RSNA.

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