Imaging Informatics

AI guidance leads to better LUS image acquisition by novice users
By Amerigo Allegretto
Healthcare professionals who are not sonography experts can successfully acquire lung ultrasound (LUS) images with AI assistance.
January 15, 2025
Ultrasound Probe2 Social
ML model using CCTA, MRI data predicts MACE in cardiac patients
By Amerigo Allegretto
A machine-learning (ML) model that includes both CCTA and stress cardiac MRI data can accurately predict major adverse cardiovascular events (MACE).
January 14, 2025
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AI will transform early cancer detection, save lives in 2025
By Roland Rott
In a new column, Roland Rott of GE HealthCare makes the case that 2025 will be a watershed year for AI in radiology.
January 13, 2025
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CEM-, radiomics-based models predict breast biopsy outcomes
By Amerigo Allegretto
Machine-learning models combining features from radiomics and contrast-enhanced mammography (CEM) can predict breast biopsy outcomes in women.
January 13, 2025
Mammo Reading Social
The John Henry Generation: The last of the radiologists, Part 2
By Joshua Ewell, DO
In part 2 of his two-part series, Joshua Ewell, DO, discuss economic pressures, philosophical and spiritual implications, and closing thoughts on the future of AI in radiology.
January 10, 2025
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The John Henry Generation: The last of the radiologists, Part 1
By Joshua Ewell, DO
In a two-part series, radiologist Joshua Ewell, DO, discusses the looming crisis in radiology, advances in AI, and and the future of radiologists.
January 9, 2025
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AI-supported mammography is safe and effective in real-world settings
By Amerigo Allegretto
A prospective study found that AI-supported mammography screening is effective in a nationwide, real-world setting.
January 8, 2025
Breast Mammo Computer 400 (1)
Deep learning plus DCE-MRI predicts breast cancer treatment response
By Amerigo Allegretto
A deep-learning system based on DCE-MRI can predict pathological complete response in breast cancer patients.
January 2, 2025
Mri Breast Cancer
Deep-learning model plus CT predicts liver decompensation in PSC patients
By Amerigo Allegretto
A deep-learning model applied to CT imaging can predict liver decompensation in patients with primary sclerosing cholangitis (PSC).
December 23, 2024
Researchers have developed a CT-based deep-learning model for predicting liver decompensation in PSC patients. Pictured is the team's diagram of the pipeline it developed for distinguishing between decompensation and no decompensation is presented in various planes. Image courtesy of Yashbir Singh, PhD.
AI improves diagnosis of acute PE, and thus patient care
By Kate Madden Yee
Using an AI-based prioritization algorithm with CTPA to diagnose acute pulmonary embolism could enhance patient care by getting imaging results to clinicians more quickly.
December 23, 2024
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Body composition analysis, MRI safety topped 2024's most read articles
By Liz Carey
While COVID and radiology salaries hit the top of AuntMinnie.com's Top 10 most-read posts of 2023, they rounded out the bottom of this year's list.
December 20, 2024
Top 10 Gold Social
Recommendations address AI bias, data transparency
By Amerigo Allegretto
New recommendations seek to make medical AI safe and effective for everyone by tackling the issues of bias and transparency.
December 18, 2024
Artificial Intelligence Ai Suit
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