Imaging Informatics

Generative AI model shows promise in chest x-ray study
By Amerigo Allegretto
A multimodal generative AI model achieved high diagnostic accuracy and showed clinical value in preliminary reporting of chest x-ray images.
March 25, 2025
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ML-based system predicts x-ray service times
By Will Morton
A team in Italy has developed a real-time machine-learning (ML) system that can predict x-ray service times in emergency departments.
March 20, 2025
Emergency
Radiologists less susceptible to visual illusions
By Will Morton
Medical image experts demonstrated superior perceptual accuracy in response to visual illusions compared with a control group.
March 20, 2025
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Do radiologists trust mammography AI software?
By Erik L. Ridley
A large-scale prospective trial exploring the use of mammography AI software in a breast cancer screening program has found that recalls were lower when cases were flagged by AI.
March 18, 2025
Full-field digital screening mammograms in participants recalled by the consensus discussion after being flagged by artificial intelligence (AI) computer-aided detection (CAD) but not by either of the two radiologists. (A) Mammogram with AI CAD score of 73 for a 55-year-old woman diagnosed with grade 3 in situ cancer and T1 (9-mm) invasive cancer in the left breast and lymph node metastasis. (B) Mammogram with AI CAD score of 54 for a 49-year-old woman diagnosed with grade 2 in situ cancer and T1 (6-mm) invasive cancer in the right breast and without lymph node metastasis. All images and caption courtesy of the RSNA.
Deep-learning plus MRI helps predict HCC recurrence
By Kate Madden Yee
Deep-learning radiopathomics models can help predict the presence of vessels encapsulating tumor clusters in hepatocellular carcinoma (HCC).
March 14, 2025
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CTC for colorectal cancer screening is at an 'inflection point'
By Kate Madden Yee
The use of CT colonography (CTC) for colorectal cancer screening is at an "inflection point" after its Medicare reimbursement approval in 2024
March 12, 2025
Colon
DL algorithm targeting prostate cancer demonstrates clinical impact
By Liz Carey
Per participant, a deep-learning (DL) algorithm demonstrated increased specificity of 64.8% compared with radiologists at 21.3%.
March 12, 2025
MRI scans show two areas of clinically significant prostate cancer in a 65-year-old man with a prostate-specific antigen level of 8.23 ng/mL. Images are (A, E) axial T2-weighted images, (B, F) axial diffusion-weighted images obtained with a b value of 1500 sec/mm2, (C, G) axial apparent diffusion coefficient images, and (D, H) lesion masks generated by the deep learning algorithm (DLA) on T2-weighted images. The DLA detected a lesion in the right posteromedial peripheral zone of the apex (D), which was assigned as Prostate Imaging Reporting and Data System (PI-RADS) 4. A radiologist also detected the lesion and assigned it PI-RADS 4 (arrow in A-C). A lesion in the right anterior transition zone (H) was detected by the DLA, which was assigned PI-RADS 4. The radiologist did not detect the lesion (arrow in E-G). Both lesions were confirmed as prostate cancer with Gleason grade group 2. Caption and image courtesy of RSNA.
AI boosts identification of incident vertebral fractures on chest CT
By Kate Madden Yee
An AI algorithm that measures 3D whole thoracic vertebral bone mineral density on conventional chest CT can help predict incident vertebral fractures.
March 11, 2025
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Deep-learning radiomics model aids in ovarian cancer diagnosis
By Amerigo Allegretto
An ensemble model combining clinical variables, O-RADS, and deep learning radiomics can diagnose ovarian tumors.
March 5, 2025
(A) SHAP summary plot shows the global contributions of variables to the model predictions. (B-E) Local explanation of the model for a single instance of ovarian endometrioid carcinoma: SHAP waterfall plot (B), ultrasound grayscale image (C), ultrasound color Doppler image (D), and 10×10 histopathological image (E). Images are available for republishing under a Creative Commons license (CC BY-NC-ND 4.0).
AI model aimed at adrenal glands, nodules could reduce workload by 10%
By Liz Carey
A future vision of AI in this context could entail analyzing adrenal nodules across multiple phases, extracting radiomic data, and providing a predictability score, study authors said.
March 4, 2025
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CNN models help determine best treatment for kids with GI obstructions
By Kate Madden Yee
Convolutional neural network (CNN) algorithms show promise for identifying gastrointestinal (GI) obstructions on x-ray in a pediatric population, researchers have reported.
February 28, 2025
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ECR: Integrating AI into radiology workflows benefits the environment
By Kate Madden Yee
Sustainability in radiology is a hot topic at this year's ECR in Vienna, and a February 27 talk focused on "green" radiology using AI technology.
February 28, 2025
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