Zebra lands AI deal with DePuy Synthes

AuntMinnie.com staff writers

Dec 18, 2019

2018 08 01 23 13 1733 Artificial Intelligence Ai 400

Artificial intelligence (AI) software developer Zebra Medical Vision has signed an agreement to provide its AI algorithms to DePuy Synthes Companies for use in analyzing orthopedic images.

Zebra's AI technology can be used to create 3D models from x-ray images. The companies believe that this could be useful for preoperative surgical planning and more cost-effective than using CT or MRI scans for presurgical planning.

Zebra's technology will be introduced as part of DePuy Synthes' Velys digital surgery system for preoperative, operative, and postoperative patient care.

Stroke Skull Xray 400

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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.

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Stroke Skull Xray 400

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.

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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.

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By AuntMinnie.com staff writers

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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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Breast Cancer 3 D

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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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