AI Medical Compendium Journal:
Journal of applied clinical medical physics

Showing 41 to 50 of 159 articles

Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm.

Journal of applied clinical medical physics May 21, 2024
BACKGROUND: CT-image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time-consuming process and inter-observer variations of manual segmentation have limited wider application in clinical practice.
Male Middle Aged Deep Learning Liver Neoplasms Female Aged Adult Image Processing, Computer-Assisted Liver Portal Vein Hepatic Veins Humans Algorithms Prognosis Retrospective Studies Tomography, X-Ray Computed
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Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms.

Journal of applied clinical medical physics May 10, 2024
BACKGROUND: The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are ...
Humans Algorithms Retrospective Studies Tomography, X-Ray Computed Lumbar Vertebrae Spinal Stenosis Dura Mater Myelography Male Female Middle Aged Aged Deep Learning Adult Image Processing, Computer-Assisted
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DenseNet model incorporating hybrid attention mechanisms and clinical features for pancreatic cystic tumor classification.

Journal of applied clinical medical physics May 7, 2024
PURPOSE: The aim of this study is to develop a deep learning model capable of discriminating between pancreatic plasma cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) by leveraging patient-specific clinical features and imaging outcomes. T...
Pancreatic Neoplasms Image Interpretation, Computer-Assisted Pancreatic Cyst Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans Algorithms Female Male
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Error detection for radiotherapy planning validation based on deep learning networks.

Journal of applied clinical medical physics May 6, 2024
BACKGROUND: Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies...
Humans Radiotherapy Planning, Computer-Assisted Quality Assurance, Health Care Radiotherapy Dosage Radiotherapy, Intensity-Modulated Algorithms Neoplasms Deep Learning Organs at Risk
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A deep learning-based 3D Prompt-nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma.

Journal of applied clinical medical physics Apr 29, 2024
PURPOSE: To create and evaluate a three-dimensional (3D) Prompt-nnUnet module that utilizes the prompts-based model combined with 3D nnUnet for producing the rapid and consistent autosegmentation of high-risk clinical target volume (HR CTV) and organ...
Humans Organs at Risk Radiotherapy Planning, Computer-Assisted Brachytherapy Radiotherapy Dosage Radiotherapy, Intensity-Modulated Female Algorithms Endometrial Neoplasms Deep Learning Prognosis Imaging, Three-Dimensional Tomography, X-Ray Computed Image Processing, Computer-Assisted
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Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.

Journal of applied clinical medical physics Apr 25, 2024
PURPOSE: To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-...
Magnetic Resonance Imaging Humans Organs at Risk Brain Neoplasms Radiotherapy Planning, Computer-Assisted Deep Learning Tomography, X-Ray Computed Radiotherapy Dosage Image Processing, Computer-Assisted Radiotherapy, Intensity-Modulated Radiometry
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Multi-scale nested UNet with transformer for colorectal polyp segmentation.

Journal of applied clinical medical physics Mar 29, 2024
BACKGROUND: Polyp detection and localization are essential tasks for colonoscopy. U-shape network based convolutional neural networks have achieved remarkable segmentation performance for biomedical images, but lack of long-range dependencies modelin...
Colorectal Neoplasms Colonoscopy Humans Image Processing, Computer-Assisted Colonic Polyps Image Interpretation, Computer-Assisted Neural Networks, Computer Algorithms Databases, Factual
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Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.

Journal of applied clinical medical physics Mar 4, 2024
PURPOSE: Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lung cancer provides important information for the feasibility of the individualized radiotherapy and allows to select the appropriate treatment strateg...
Positron Emission Tomography Computed Tomography Neoplasm Recurrence, Local Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Radiosurgery Prognosis Middle Aged Retrospective Studies Image Processing, Computer-Assisted Female Lung Neoplasms Humans Aged Carcinoma, Non-Small-Cell Lung Machine Learning Aged, 80 and over Algorithms Radiomics Male
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Deep-learning-based renal artery stenosis diagnosis via multimodal fusion.

Journal of applied clinical medical physics Feb 19, 2024
PURPOSE: Diagnosing Renal artery stenosis (RAS) presents challenges. This research aimed to develop a deep learning model for the computer-aided diagnosis of RAS, utilizing multimodal fusion technology based on ultrasound scanning images, spectral wa...
Ultrasonography, Doppler, Color Humans Renal Artery Obstruction Deep Learning Angiography
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A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images.

Journal of applied clinical medical physics Feb 19, 2024
PURPOSE: Deep learning-based auto-segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual labor. Over the past decade, convolutional neural networks (CNNs) have become prominent in medical ...
Algorithms Humans Tomography, X-Ray Computed Image Processing, Computer-Assisted Neural Networks, Computer Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung
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