AI Medical Compendium Journal:
Journal of applied clinical medical physics

Showing 11 to 20 of 159 articles

Performance of recurrent neural networks with Monte Carlo dropout for predicting pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging data.

Journal of applied clinical medical physics Dec 23, 2024
PURPOSE: To quantitatively evaluate the performance of two types of recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent units (GRU), using Monte Carlo dropout (MCD) to predict pharmacokinetic (PK) parameters from dynam...
Recurrent Neural Networks Computer Simulation Humans Magnetic Resonance Imaging Neural Networks, Computer Image Processing, Computer-Assisted Brain Neoplasms Radiotherapy Planning, Computer-Assisted Algorithms Contrast Media Monte Carlo Method
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Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer.

Journal of applied clinical medical physics Dec 20, 2024
PURPOSE: Cardiotoxicity is one of the major concerns in breast cancer treatment, significantly affecting patient outcomes. To improve the likelihood of favorable outcomes for breast cancer survivors, it is essential to carefully balance the potential...
Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Cardiotoxicity Unilateral Breast Neoplasms Radiotherapy, Intensity-Modulated Radiation Injuries Radiomics Echocardiography Middle Aged Aged Adult Humans Breast Neoplasms Machine Learning Female
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Estimation of heart dose in left breast cancer radiotherapy: Assessment of vDIBH feasibility using the supervised machine learning algorithm.

Journal of applied clinical medical physics Dec 6, 2024
BACKGROUND AND OBJECTIVE: The volunteer deep inspiration breath hold (vDIBH) technique is used to reduce the heart dose in left breast cancer radiotherapy. Many times, it is faced that despite rigorous exercise and training, not all patients get bene...
Unilateral Breast Neoplasms Feasibility Studies Supervised Machine Learning Organs at Risk Radiotherapy, Intensity-Modulated Breath Holding Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Adult Breast Neoplasms Humans Middle Aged Algorithms Heart Female
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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...
Abdomen Tomography, X-Ray Computed Magnetic Resonance Imaging Deep Learning Federated Learning Image Processing, Computer-Assisted Humans Algorithms
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Assessing population-based to personalized planning strategies for head and neck adaptive radiotherapy.

Journal of applied clinical medical physics Dec 3, 2024
PURPOSE: Optimal head-and-neck cancer (HNC) treatment planning requires accurate and feasible planning goals to meet dosimetric constraints and generate robust online adaptive treatment plans. A new x-ray-based adaptive radiotherapy (ART) treatment p...
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Retrospective Studies Head and Neck Neoplasms Organs at Risk Algorithms Precision Medicine Humans Artificial Intelligence
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Low dose threshold for measuring cardiac functional metrics using four-dimensional CT with deep learning.

Journal of applied clinical medical physics Dec 3, 2024
BACKGROUND: Four-dimensional CT is increasingly used for functional cardiac imaging, including prognosis for conditions such as heart failure and post myocardial infarction. However, radiation dose from an acquisition spanning the full cardiac cycle ...
Humans Heart Radiation Dosage Four-Dimensional Computed Tomography Algorithms Deep Learning Image Processing, Computer-Assisted
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Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.

Journal of applied clinical medical physics Dec 1, 2024
PURPOSE: This study aimed to investigate the dosimetric impact of deep-learning-based auto-contouring for clinical target volume (CTV) and organs at risk (OARs) delineation in prostate cancer radiotherapy planning. Additionally, we compared the geome...
Humans Male Deep Learning Feasibility Studies Retrospective Studies Organs at Risk Tomography, X-Ray Computed Radiotherapy Planning, Computer-Assisted Observer Variation Imaging, Three-Dimensional Prostatic Neoplasms Prostate Radiotherapy Dosage Radiotherapy, Intensity-Modulated Radiometry
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Quality and mechanical efficiency of automated knowledge-based planning for volumetric-modulated arc therapy in head and neck cancer.

Journal of applied clinical medical physics Dec 1, 2024
OBJECTIVES: This study aimed to examine the effectiveness of the automated RapidPlan in assessing plan quality and to explore how beam complexity affects the mechanical performance of volumetric modulated arc therapy for head and neck cancers.
Humans Radiotherapy Dosage Radiotherapy, Intensity-Modulated Organs at Risk Radiotherapy Planning, Computer-Assisted Quality Assurance, Health Care Algorithms Head and Neck Neoplasms Knowledge Bases
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LightAWNet: Lightweight adaptive weighting network based on dynamic convolutions for medical image segmentation.

Journal of applied clinical medical physics Dec 1, 2024
PURPOSE: The complexity of convolutional neural networks (CNNs) can lead to improved segmentation accuracy in medical image analysis but also results in increased network complexity and training challenges, especially under resource limitations. Conv...
Humans Neural Networks, Computer Algorithms Image Processing, Computer-Assisted
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Evaluation and comparison of synthetic computed tomography algorithms with 3T MRI for prostate radiotherapy: AI-based versus bulk density method.

Journal of applied clinical medical physics Nov 29, 2024
PURPOSE: Synthetic computed tomography (sCT)-algorithms, which generate computed tomography images from magnetic resonance imaging data, are becoming part of the clinical radiotherapy workflow. The aim of this retrospective study was to evaluate and ...
Image Processing, Computer-Assisted Retrospective Studies Radiotherapy Dosage Magnetic Resonance Imaging Prostatic Neoplasms Tomography, X-Ray Computed Humans Radiotherapy, Intensity-Modulated Organs at Risk Artificial Intelligence Male Radiotherapy Planning, Computer-Assisted Algorithms
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