AIMC Topic: Abdomen
Clear Filters Showing 11 to 20 of 240 articles
Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen.
Medical physics
Dec 19, 2024
BACKGROUND: Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since the...
Automated Classification of Body MRI Sequences Using Convolutional Neural Networks.
Academic radiology
Dec 6, 2024
RATIONALE AND OBJECTIVES: Multi-parametric MRI (mpMRI) studies of the body are routinely acquired in clinical practice. However, a standardized naming convention for MRI protocols and series does not exist currently. Conflicts in the series descripti...
Personalized federated learning for abdominal multi-organ segmentation based on frequency domain aggregation.
Journal of applied clinical medical physics
Dec 5, 2024
PURPOSE: The training of deep learning (DL) models in medical images requires large amounts of sensitive patient data. However, acquiring adequately labeled datasets is challenging because of the heavy workload of manual annotations and the stringent...
Deep learning reconstruction for accelerated high-resolution upper abdominal MRI improves lesion detection without time penalty.
Diagnostic and interventional imaging
Nov 19, 2024
PURPOSE: The purpose of this study was to compare a conventional T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence with a DL-reconstructed accelerated high-resolution VIBE sequence (HR-VIBE) in terms of image quality, lesion...
Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.
Tomography (Ann Arbor, Mich.)
Nov 18, 2024
Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of...
PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans.
Nature communications
Nov 7, 2024
Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity. We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. The model is trained on abd...
Image quality in three-dimensional (3D) contrast-enhanced dynamic magnetic resonance imaging of the abdomen using deep learning denoising technique: intraindividual comparison between T1-weighted sequences with compressed sensing and with a modified Fast 3D mode wheel.
Japanese journal of radiology
Nov 6, 2024
PURPOSE: To assess the image quality of a modified Fast three-dimensional (Fast 3D) mode wheel with sequential data filling (mFast 3D wheel) combined with a deep learning denoising technique (Advanced Intelligent Clear-IQ Engine [AiCE]) in contrast-e...
Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications.
Abdominal radiology (New York)
Nov 2, 2024
BACKGROUND: In recent years, the integration of artificial intelligence (AI) techniques into medical imaging has shown great potential to transform the diagnostic process. This review aims to provide a comprehensive overview of current state-of-the-a...
[Development of an abdominal acupoint localization system based on AI deep learning].
Zhongguo zhen jiu = Chinese acupuncture & moxibustion
Oct 28, 2024
This study aims to develop an abdominal acupoint localization system based on computer vision and convolutional neural networks (CNNs). To address the challenge of abdominal acupoint localization, a multi-task CNNs architecture was constructed and tr...
Accelerated 2D radial Look-Locker T1 mapping using a deep learning-based rapid inversion recovery sampling technique.
NMR in biomedicine
Oct 2, 2024
Efficient abdominal coverage with T1-mapping methods currently available in the clinic is limited by the breath hold period (BHP) and the time needed for T1 recovery. This work develops a T1-mapping framework for efficient abdominal coverage based on...
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823