Imaging Informatics: Page 23

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
Ai Handshake
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.
April 16, 2024
Ai
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
Bmd
Vision transformer AI model boosts PET/CT imaging of cancer
By Will Morton
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
By Will Morton
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
Lung Ai
UltraCon: Ultrasound empowers patients with prosthetic limbs
By Amerigo Allegretto
Gil Weinberg, PhD, a founding director of Georgia Tech's Center for Music Technology, spoke with AuntMinnie.com on ultrasound's use in prosthetics.
April 9, 2024
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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.
MEG reveals how the brain processes face-like images
By Will Morton
A brain imaging technique called magnetoencephalography (MEG) has identified a neural network that plays a key role in face pareidolia – a tendency to see faces in nonface images.
April 8, 2024
Face Images Thumbnail
Dartmouth event bats around promise, shortcomings of AI in healthcare
By AuntMinnie.com staff writers
Dartmouth's Center for Precision Health and Artificial Intelligence held its first symposium April 3 featuring leaders from radiology, pathology, and more.
April 5, 2024
Curtis Langlotz, MD, PhD, professor of radiology, medicine, and biomedical data science at Stanford University, was among the keynote speakers at the Dartmouth event April 3. Photo courtesy of Dartmouth.
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