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Radiologists' trust of AI is critical for hospital adoption
It wasn't that long ago that artificial intelligence (AI) proponents perpetuated the notion of AI marketplaces, believing that they provide a more straightforward way to purchase the technology that would soon lead to widespread adoption in hospital systems. But acquiring AI algorithms has never been the most challenging aspect of AI adoption.
January 25, 2022
2022 01 25 18 07 6201 Batman Sinan
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MRI
Deep-learning technique boosts diagnostic ability of fast knee MRI
A new, fast deep learning-based MR imaging technique shows excellent performance for diagnosing knee injuries, researchers have reported.
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Molecular Imaging
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CT
Quantitative CT textures of the lung help assess COPD severity
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Health data restrictions plague medtech AI developers
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Deep-learning technique boosts diagnostic ability of fast knee MRI
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A new, fast deep learning-based MR imaging technique shows excellent performance for diagnosing knee injuries, researchers have reported.
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Most veterans adhere to post-EVAR surveillance imaging
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By Kate Madden Yee
Quantitative CT can capture lung inflammation patterns to assess the severity of chronic obstructive pulmonary disease (COPD).
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By Liz Carey
Medtech industry and health leaders from big tech have introduced a new AI Policy Roadmap.
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ACS: Breast cancer screening rates have recovered post-COVID
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Lung POCUS helps diagnose dyspnea in emergency departments
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Images show lung ultrasound findings of major pleuro-pulmonary pathologies. (a) Pleural effusion; (b) Pneumonia; (c) Bronchopneumonia; (d) Acute pulmonary edema; (e) Chronic obstructive pulmonary disease exacerbation; (f) Pleuropulmonary tumors; (g) Acute respiratory distress syndrome.
MRI tool segments over 60 abdominal organs, structures
By Amerigo Allegretto
An automated multiorgan tool can accurately segment T1-weighted abdominal MR images.
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AI produces synthetic 3T MR images from portable scans
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Figure: Improved image quality with a generative adversarial network architecture for low- to high-field-strength image translation, called LowGAN. (A–C) Top: High-field-strength (3-T) and low-field-strength (64-mT) images and LowGAN outputs for a single participant across (A) fluid-attenuated inversion recovery (FLAIR), (B) T1-weighted (T1w), and (C) T2-weighted (T2w) contrasts. Bottom: Graphs demonstrate the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and feature similarity index (FSIM) between high field strength and low field strength (64-mT) and high field strength and LowGAN outputs. a.u. = arbitrary units.****p ≤ 0.0001. Images and caption courtesy of Radiology and the RSNA.
Machine learning standardizes bone tumor grading on MRI, CT
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Accelerated MRI protocols reduce environmental impact
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Ultrasound-based model finds endometrial malignancies
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Clinical and ultrasound features improve diagnostic accuracy for detecting endometrial malignancy.
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Ultrasound Gynecologist
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