AIMC Topic: Radiography
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Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy.
International journal of computer assisted radiology and surgery
Feb 15, 2023
PURPOSE: Minimally invasive treatments for renal carcinoma offer a low rate of complications and quick recovery. One drawback of the use of computed tomography (CT) for needle guidance is the use of iodinated contrast agents, which require an increas...
Artificial Intelligence in Radiology: Overview of Application Types, Design, and Challenges.
Seminars in roentgenology
Feb 15, 2023
Algorithmic encoding of protected characteristics in chest X-ray disease detection models.
EBioMedicine
Feb 13, 2023
BACKGROUND: It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. An algorithm may encode protected characteristics, and then use this information for making predictions due to undesirable...
Deep Learning Algorithm for Identifying Cervical Cord Compression Due to Degenerative Canal Stenosis on Radiography.
Spine
Feb 10, 2023
STUDY DESIGN: Cross-sectional study.
Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network.
Scientific reports
Feb 9, 2023
The generative adversarial network (GAN) is a promising deep learning method for generating images. We evaluated the generation of highly realistic and high-resolution chest radiographs (CXRs) using progressive growing GAN (PGGAN). We trained two PGG...
Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM).
Neuroradiology
Feb 7, 2023
PURPOSE: The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) is a recently released guideline designed for the optimal reporting methodology of artificial intelligence (AI) studies. Gliomas are the most common form of primary maligna...
Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial.
Korean journal of radiology
Feb 6, 2023
OBJECTIVE: It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can enhance the accuracy of chest radiograph (CR) interpretation in real-world clinical practice. We aimed to compare the accuracy of CR interpretation a...
Multiclass datasets expand neural network utility: an example on ankle radiographs.
International journal of computer assisted radiology and surgery
Feb 2, 2023
PURPOSE: Artificial intelligence in computer vision has been increasingly adapted in clinical application since the implementation of neural networks, potentially providing incremental information beyond the mere detection of pathology. As its algori...
Improving Anatomical Plausibility in Medical Image Segmentation via Hybrid Graph Neural Networks: Applications to Chest X-Ray Analysis.
IEEE transactions on medical imaging
Feb 2, 2023
Anatomical segmentation is a fundamental task in medical image computing, generally tackled with fully convolutional neural networks which produce dense segmentation masks. These models are often trained with loss functions such as cross-entropy or D...
Deep learning in sex estimation from knee radiographs - A proof-of-concept study utilizing the Terry Anatomical Collection.
Legal medicine (Tokyo, Japan)
Jan 31, 2023
Although knee measurements yield high classification rates in metric sex estimation, there is a paucity of studies exploring the knee in artificial intelligence-based sexing. This proof-of-concept study aimed to develop deep learning algorithms for s...
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