AIMC Topic: Radiation Injuries

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An Intelligent Robot Detection System of Uncontrolled Radioactive Sources.

Computational intelligence and neuroscience Sep 19, 2022
In recent years, radioactive sources have been widely used in various fields (e.g., nuclear industry, agriculture, medical industry, environmental protection, and scientific research) and successfully applied to develop scientific projects, such as n...
Sewage Radiation Injuries Humans Robotics
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3D Segmentation Guided Style-Based Generative Adversarial Networks for PET Synthesis.

IEEE transactions on medical imaging Aug 1, 2022
Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a concern, whereas the quality of low-dose images is never desirable for clinical use. So it is of great interest to translate low-dose PET images into full-...
Image Processing, Computer-Assisted Positron-Emission Tomography Radiation Dosage Neural Networks, Computer Radiation Injuries Deep Learning
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A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy.

Scientific reports May 27, 2022
Early regression-the regression in tumor volume during the initial phase of radiotherapy (approximately 2 weeks after treatment initiation)-is a common occurrence during radiotherapy. This rapid radiation-induced tumor regression may alter target coo...
Neural Networks, Computer Humans Radiation Injuries Tomography, X-Ray Computed Deep Learning Head and Neck Neoplasms
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Evaluation of a delineation software for cardiac atlas-based autosegmentation: An example of the use of artificial intelligence in modern radiotherapy.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique Nov 2, 2020
PURPOSE: The primary objective of this work was to implement and evaluate a cardiac atlas-based autosegmentation technique based on the "Workflow Box" software (Mirada Medical, Oxford UK), in order to delineate cardiac substructures according to Euro...
Heart Ventricles Radiation Injuries Software Humans Medical Illustration Software Validation Artificial Intelligence Female Unilateral Breast Neoplasms Heart
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Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.

BMC cancer Jun 1, 2020
BACKGROUND: Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI.
Longitudinal Studies Magnetic Resonance Imaging Brain Injuries Male Middle Aged ROC Curve Image Interpretation, Computer-Assisted Radiation Injuries Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Predictive Value of Tests Aftercare Models, Biological Humans Machine Learning Female Adult Algorithms Retrospective Studies Temporal Lobe
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Radiomics and deep learning in lung cancer.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] May 4, 2020
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomograp...
Imaging Genomics Radiosurgery Radiotherapy Deep Learning Radiation Injuries Phenotype Image Processing, Computer-Assisted Neoplasm Recurrence, Local Databases, Factual Lung Neoplasms Radiotherapy Planning, Computer-Assisted False Positive Reactions Treatment Outcome Prognosis Neoplasm Staging Tomography, X-Ray Computed Genotype Humans Computational Biology Forecasting
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A novel deep learning model using dosimetric and clinical information for grade 4 radiotherapy-induced lymphopenia prediction.

Physics in medicine and biology Feb 4, 2020
Radiotherapy-induced lymphopenia has increasingly been shown to reduce cancer survivorship. We developed a novel hybrid deep learning model to efficiently integrate an entire set of dosimetric parameters of a radiation treatment plan with a patient's...
Humans Deep Learning Models, Statistical Radiation Injuries Radiometry Lymphopenia Esophageal Neoplasms Radiotherapy
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Overlooked pitfalls in multi-class machine learning classification in radiation oncology and how to avoid them.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Jan 25, 2020
In radiation oncology, Machine Learning classification publications are typically related to two outcome classes, e.g. the presence or absence of distant metastasis. However, multi-class classification problems also have great clinical relevance, e.g...
Monte Carlo Method Machine Learning Radiation Injuries Biomarkers Radiation Exposure Risk Factors Area Under Curve Models, Theoretical Radiation Oncology Technology Assessment, Biomedical Humans
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Detection and Monitoring of Thermal Lesions Induced by Microwave Ablation Using Ultrasound Imaging and Convolutional Neural Networks.

IEEE journal of biomedical and health informatics Sep 6, 2019
Microwave ablation (MWA) for cancer treatment is frequently monitored by ultrasound (US) B-mode imaging in the clinic, which often fails due to the low intrinsic contrast between the thermal lesion and normal tissue. Deep learning, especially convolu...
Surgery, Computer-Assisted Deep Learning Image Interpretation, Computer-Assisted Radiation Injuries Animals Ultrasonography Neural Networks, Computer Microwaves Radiofrequency Ablation Liver Swine
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Cath Lab Robotics: Paradigm Change in Interventional Cardiology?

Current cardiology reports Aug 31, 2019
PURPOSE OF REVIEW: To review the contemporary evidence for robotic-assisted percutaneous coronary and vascular interventions, discussing its current capabilities, limitations, and potential future applications.
Therapy, Computer-Assisted Coronary Angiography Cardiology Humans Percutaneous Coronary Intervention Radiation Injuries Treatment Outcome Coronary Artery Disease Radiation Exposure Robotics
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