AI Medical Compendium Journal:
Physical and engineering sciences in medicine

Showing 21 to 30 of 88 articles

Are deep learning classification results obtained on CT scans fair and interpretable?

Physical and engineering sciences in medicine Apr 4, 2024
Following the great success of various deep learning methods in image and object classification, the biomedical image processing society is also overwhelmed with their applications to various automatic diagnosis cases. Unfortunately, most of the deep...
Neural Networks, Computer Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Humans
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Machine learning-based analysis of Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade.

Physical and engineering sciences in medicine Mar 25, 2024
Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications an...
Aged Male Middle Aged Humans Machine Learning Prostatic Neoplasms Neoplasm Grading Edetic Acid ROC Curve Image Processing, Computer-Assisted Positron Emission Tomography Computed Tomography Oligopeptides Gallium Radioisotopes Gallium Isotopes
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Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients.

Physical and engineering sciences in medicine Mar 21, 2024
Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundarie...
Fluorodeoxyglucose F18 Image Processing, Computer-Assisted Lymphoma Humans Fuzzy Logic Neural Networks, Computer Lymphoma, Large B-Cell, Diffuse Supervised Machine Learning Positron-Emission Tomography Automation
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Pattern classification of interstitial lung diseases from computed tomography images using a ResNet-based network with a split-transform-merge strategy and split attention.

Physical and engineering sciences in medicine Mar 4, 2024
In patients with interstitial lung disease (ILD), accurate pattern assessment from their computed tomography (CT) images could help track lung abnormalities and evaluate treatment efficacy. Based on excellent image classification performance, convolu...
Algorithms Tomography, X-Ray Computed Image Processing, Computer-Assisted Humans Neural Networks, Computer Lung Diseases, Interstitial Pattern Recognition, Automated
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Segmentation of liver and liver lesions using deep learning.

Physical and engineering sciences in medicine Feb 21, 2024
Segmentation of organs and lesions could be employed for the express purpose of dosimetry in nuclear medicine, assisted image interpretations, and mass image processing studies. Deep leaning created liver and liver lesion segmentation on clinical 3D ...
Magnetic Resonance Imaging Liver Liver Neoplasms Imaging, Three-Dimensional Image Processing, Computer-Assisted Male Humans Middle Aged Female Deep Learning
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Examining arterial pulsation to identify and risk-stratify heart failure subjects with deep neural network.

Physical and engineering sciences in medicine Feb 15, 2024
Hemodynamic parameters derived from pulse wave analysis have been shown to predict long-term outcomes in patients with heart failure (HF). Here we aimed to develop a deep-learning based algorithm that incorporates pressure waveforms for the identific...
Neural Networks, Computer Female Aged Male Middle Aged Risk Assessment Humans Arteries Pulse Wave Analysis Case-Control Studies Heart Failure Deep Learning Proportional Hazards Models ROC Curve
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Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal.

Physical and engineering sciences in medicine Feb 15, 2024
In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) pa...
Selective Serotonin Reuptake Inhibitors Treatment Outcome Electroencephalography Signal Processing, Computer-Assisted Middle Aged Depressive Disorder, Major Deep Learning Humans Neural Networks, Computer Female Adult Male
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A Q-transform-based deep learning model for the classification of atrial fibrillation types.

Physical and engineering sciences in medicine Feb 14, 2024
According to the World Health Organization (WHO), Atrial Fibrillation (AF) is emerging as a global epidemic, which has resulted in a need for techniques to accurately diagnose AF and its various subtypes. While the classification of cardiac arrhythmi...
Atrial Fibrillation Humans Neural Networks, Computer Deep Learning Electrocardiography Signal Processing, Computer-Assisted
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CT angiography prior to endovascular procedures: can artificial intelligence improve reporting?

Physical and engineering sciences in medicine Jan 31, 2024
CT angiography prior to endovascular aortic surgery is the standard non-invasive imaging method for evaluation of aortic dimensions and access sites. A detailed report is crucial to a proper planning. We assessed Artificial Intelligence (AI)-algorith...
Male Humans Female Computed Tomography Angiography Endovascular Procedures Aged Transcatheter Aortic Valve Replacement Artificial Intelligence Aorta Aged, 80 and over Algorithms
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Inter-fractional portability of deep learning models for lung target tracking on cine imaging acquired in MRI-guided radiotherapy.

Physical and engineering sciences in medicine Jan 10, 2024
MRI-guided radiotherapy systems enable beam gating by tracking the target on planar, two-dimensional cine images acquired during treatment. This study aims to evaluate how deep-learning (DL) models for target tracking that are trained on data from on...
Image Processing, Computer-Assisted Lung Neoplasms Radiotherapy, Image-Guided Magnetic Resonance Imaging, Cine Humans Algorithms Lung Deep Learning
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