AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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High-resolution deep learning reconstruction to improve the accuracy of CT fractional flow reserve.

European radiology
OBJECTIVES: This study aimed to compare the diagnostic performance of CT-derived fractional flow reserve (CT-FFR) using model-based iterative reconstruction (MBIR) and high-resolution deep learning reconstruction (HR-DLR) images to detect functionall...

Development of a deep-learning algorithm for etiological classification of subarachnoid hemorrhage using non-contrast CT scans.

European radiology
OBJECTIVES: This study aims to develop a deep learning algorithm for differentiating aneurysmal subarachnoid hemorrhage (aSAH) from non-aneurysmal subarachnoid hemorrhage (naSAH) using non-contrast computed tomography (NCCT) scans.

Impact of test set composition on AI performance in pediatric wrist fracture detection in X-rays.

European radiology
OBJECTIVES: To evaluate how different test set sampling strategies-random selection and balanced sampling-affect the performance of artificial intelligence (AI) models in pediatric wrist fracture detection using radiographs, aiming to highlight the n...

Automated CT segmentation for lower extremity tissues in lymphedema evaluation using deep learning.

European radiology
OBJECTIVES: Clinical assessment of lymphedema, particularly for lymphedema severity and fluid-fibrotic lesions, remains challenging with traditional methods. We aimed to develop and validate a deep learning segmentation tool for automated tissue comp...