AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities.
Journal of computer assisted tomography
May 13, 2025
Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of A...
AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia.
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: To quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course.
Male
Middle Aged
Lung
Severity of Illness Index
Adolescent
Evaluation Studies as Topic
Radiographic Image Interpretation, Computer-Assisted
Time
Aged
Aged, 80 and over
Young Adult
Adult
Sensitivity and Specificity
Tomography, X-Ray Computed
Female
Reproducibility of Results
SARS-CoV-2
Humans
Artificial Intelligence
COVID-19
Retrospective Studies
Use of a Dual Artificial Intelligence Platform to Detect Unreported Lung Nodules.
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: To investigate the performance of Dual-AI Deep Learning Platform in detecting unreported pulmonary nodules that are 6 mm or greater, comprising computer-vision (CV) algorithm to detect pulmonary nodules, with positive results filtered by n...
Artificial intelligence-based pulmonary vessel segmentation: an opportunity for automated three-dimensional planning of lung segmentectomy.
Interdisciplinary cardiovascular and thoracic surgery
May 6, 2025
OBJECTIVES: This study aimed to develop an automated method for pulmonary artery and vein segmentation in both left and right lungs from computed tomography (CT) images using artificial intelligence (AI). The segmentations were evaluated using PulmoS...
Artificial intelligence-based automated matching of pulmonary nodules on follow-up chest CT.
European radiology experimental
May 2, 2025
BACKGROUND: The growing demand for follow-up imaging highlights the need for tools supporting the assessment of pulmonary nodules over time. We evaluated the performance of an artificial intelligence (AI)-based system for automated nodule matching.
Diagnostic Accuracy of On-Premise Automated Coronary CT Angiography Analysis Based on Coronary Artery Disease Reporting and Data System 2.0.
Radiology
May 1, 2025
Background Chest pain is a leading cause of outpatient and emergency department visits; advancements in artificial intelligence (AI) could improve coronary CT angiography (CCTA) workflows for these patients. Purpose To evaluate the performance of an ...
Development and Validation of a Sham-AI Model for Intracranial Aneurysm Detection at CT Angiography.
Radiology. Artificial intelligence
May 1, 2025
Purpose To evaluate a sham-artificial intelligence (AI) model acting as a placebo control for a standard-AI model for diagnosis of intracranial aneurysm. Materials and Methods This retrospective crossover, blinded, multireader, multicase study was co...
External Testing of a Commercial AI Algorithm for Breast Cancer Detection at Screening Mammography.
Radiology. Artificial intelligence
May 1, 2025
Purpose To test a commercial artificial intelligence (AI) system for breast cancer detection at the BC Cancer Breast Screening Program. Materials and Methods In this retrospective study of 136 700 female individuals (mean age, 58.8 years ± 9.4 [SD]; ...
Performance of Two Deep Learning-based AI Models for Breast Cancer Detection and Localization on Screening Mammograms from BreastScreen Norway.
Radiology. Artificial intelligence
May 1, 2025
Purpose To evaluate cancer detection and marker placement accuracy of two artificial intelligence (AI) models developed for interpretation of screening mammograms. Materials and Methods This retrospective study included data from 129 434 screening ex...
Radiomics-based automated machine learning for differentiating focal liver lesions on unenhanced computed tomography.
Abdominal radiology (New York)
May 1, 2025
BACKGROUND & AIMS: Enhanced computed tomography (CT) is the primary method for focal liver lesion diagnosis. We aimed to use automated machine learning (AutoML) algorithms to differentiate between benign and malignant focal liver lesions on the basis...
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