AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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A multi-model deep learning approach for the identification of coronary artery calcifications within 2D coronary angiography images.
International journal of computer assisted radiology and surgery
Jun 1, 2025
PURPOSE: Identifying and quantifying coronary artery calcification (CAC) is crucial for preoperative planning, as it helps to estimate both the complexity of the 2D coronary angiography (2DCA) procedure and the risk of developing intraoperative compl...
Deep learning for liver lesion segmentation and classification on staging CT scans of colorectal cancer patients: a multi-site technical validation study.
Clinical radiology
Jun 1, 2025
AIM: To validate a liver lesion detection and classification model using staging computed tomography (CT) scans of colorectal cancer (CRC) patients.
MEF-Net: Multi-scale and edge feature fusion network for intracranial hemorrhage segmentation in CT images.
Computers in biology and medicine
Jun 1, 2025
Intracranial Hemorrhage (ICH) refers to cerebral bleeding resulting from ruptured blood vessels within the brain. Delayed and inaccurate diagnosis and treatment of ICH can lead to fatality or disability. Therefore, early and precise diagnosis of intr...
Pediatric chest X-ray diagnosis using neuromorphic models.
Computers in biology and medicine
Jun 1, 2025
This research presents an innovative neuromorphic method utilizing Spiking Neural Networks (SNNs) to analyze pediatric chest X-rays (PediCXR) to identify prevalent thoracic illnesses. We incorporate spiking-based machine learning models such as Spiki...
AI-supported approaches for mammography single and double reading: A controlled multireader study.
European journal of radiology
Jun 1, 2025
PURPOSE: To assess the impact of artificial intelligence (AI) on the diagnostic performance of radiologists with varying experience levels in mammography reading, considering single and simulated double reading approaches.
Deep learning-driven multi-class classification of brain strokes using computed tomography: A step towards enhanced diagnostic precision.
European journal of radiology
Jun 1, 2025
OBJECTIVE: To develop and validate deep learning models leveraging CT imaging for the prediction and classification of brain stroke conditions, with the potential to enhance accuracy and support clinical decision-making.
Keeping AI on Track: Regular monitoring of algorithmic updates in mammography.
European journal of radiology
Jun 1, 2025
PURPOSE: To demonstrate a method of benchmarking the performance of two consecutive software releases of the same commercial artificial intelligence (AI) product to trained human readers using the Personal Performance in Mammographic Screening scheme...
AI for fracture diagnosis in clinical practice: Four approaches to systematic AI-implementation and their impact on AI-effectiveness.
European journal of radiology
Jun 1, 2025
PURPOSE: Artificial Intelligence (AI) has been shown to enhance fracture-detection-accuracy, but the most effective AI-implementation in clinical practice is less well understood. In the current study, four approaches to AI-implementation are evaluat...
Automated determination of hip arthrosis on the Kellgren-Lawrence scale in pelvic digital radiographs scans using machine learning.
Computer methods and programs in biomedicine
Jun 1, 2025
BACKGROUND AND OBJECTIVE: Automated analysis of digital radiographs of the pelvis to determine the hip arthrosis state in concordance with the Kellgren-Lawrence scale could facilitate and standardize radiogram descriptions.
Review and reflections on live AI mammographic screen reading in a large UK NHS breast screening unit.
Clinical radiology
Jun 1, 2025
UNLABELLED: The Radiology team from a large Breast Screening Unit in the UK with a screening population of over 135,000 took part in a service evaluation project using artificial intelligence (AI) for reading breast screening mammograms.
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