AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction.
Abdominal radiology (New York)
May 1, 2025
OBJECTIVES: To evaluate the effectiveness of super-resolution deep learning reconstruction (SR-DLR) in low-dose abdominal computed tomography (CT) imaging compared with hybrid iterative reconstruction (HIR) and conventional deep learning reconstructi...
Feasibility of deep learning algorithm in diagnosing lumbar central canal stenosis using abdominal CT.
Skeletal radiology
May 1, 2025
OBJECTIVE: To develop a deep learning algorithm for diagnosing lumbar central canal stenosis (LCCS) using abdominal CT (ACT) and lumbar spine CT (LCT).
Fully Automated Agatston Score Calculation From Electrocardiography-Gated Cardiac Computed Tomography Using Deep Learning and Multi-Organ Segmentation: A Validation Study.
Angiology
May 1, 2025
To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neur...
Deep Learning
Reproducibility of Results
Electrocardiography
Coronary Artery Disease
Humans
Aged
Predictive Value of Tests
Neural Networks, Computer
Male
Coronary Angiography
Retrospective Studies
Vascular Calcification
Middle Aged
Radiographic Image Interpretation, Computer-Assisted
Tomography, X-Ray Computed
Computed Tomography Angiography
Female
Cardiac-Gated Imaging Techniques
Generalizability of AI-based image segmentation and centering estimation algorithm: a multi-region, multi-center, and multi-scanner study.
Radiation protection dosimetry
Apr 22, 2025
We created and validated an open-access AI algorithm (AIc) for assessing image segmentation and patient centering in a multi-body-region, multi-center, and multi-scanner study. Our study included 825 head, chest, and abdomen-pelvis CT from 275 patien...
Effect of adaptive statistical iterative reconstruction-V algorithm and deep learning image reconstruction algorithm on image quality and emphysema quantification in COPD patients under ultra-low-dose conditions.
The British journal of radiology
Apr 1, 2025
PURPOSE: To explore the effect of different reconstruction algorithms (ASIR-V and DLIR) on image quality and emphysema quantification in chronic obstructive pulmonary disease (COPD) patients under ultra-low-dose scanning conditions.
Evaluation of a Multi-Instant Multimodal Artificial Intelligence System Supporting Interpretive and Noninterpretive Functions.
Journal of breast imaging
Mar 18, 2025
OBJECTIVE: Artificial intelligence (AI) has been shown to hold promise for improving breast cancer screening, offering advanced capabilities to enhance diagnostic accuracy and efficiency. This study aimed to evaluate the impact of a multimodal multi-...
Artificial Intelligence-based Software for Breast Arterial Calcification Detection on Mammograms.
Journal of breast imaging
Mar 18, 2025
OBJECTIVE: The performance of a commercially available artificial intelligence (AI)-based software that detects breast arterial calcifications (BACs) on mammograms is presented.
Diagnostic Accuracy and Clinical Value of a Domain-specific Multimodal Generative AI Model for Chest Radiograph Report Generation.
Radiology
Mar 1, 2025
Background Generative artificial intelligence (AI) is anticipated to alter radiology workflows, requiring a clinical value assessment for frequent examinations like chest radiograph interpretation. Purpose To develop and evaluate the diagnostic accur...
Human-AI Interaction in the ScreenTrustCAD Trial: Recall Proportion and Positive Predictive Value Related to Screening Mammograms Flagged by AI CAD versus a Human Reader.
Radiology
Mar 1, 2025
Background The ScreenTrustCAD trial was a prospective study that evaluated the cancer detection rates for combinations of artificial intelligence (AI) computer-aided detection (CAD) and two radiologists. The results raised concerns about the tendency...
Value of Using a Generative AI Model in Chest Radiography Reporting: A Reader Study.
Radiology
Mar 1, 2025
Background Multimodal generative artificial intelligence (AI) technologies can produce preliminary radiology reports, and validation with reader studies is crucial for understanding the clinical value of these technologies. Purpose To assess the clin...
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