AIMC Topic: Tomography, Emission-Computed, Single-Photon
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Gated SPECT-Derived Myocardial Strain Estimated From Deep-Learning Image Translation Validated From N-13 Ammonia PET.
Academic radiology
Aug 2, 2024
RATIONALE AND OBJECTIVES: This study investigated the use of deep learning-generated virtual positron emission tomography (PET)-like gated single-photon emission tomography (SPECT) for assessing myocardial strain, overcoming limitations of convention...
Development and Validation of an Explainable Machine Learning Model for Identification of Hyper-Functioning Parathyroid Glands from High-Frequency Ultrasonographic Images.
Ultrasound in medicine & biology
Jul 25, 2024
OBJECTIVE: To develop and validate a machine learning (ML) model based on high-frequency ultrasound (HFUS) images with the aim to identify the functional status of parathyroid glands (PTGs) in secondary hyper-parathyroidism (SHPT) patients.
Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images.
Skeletal radiology
Jul 19, 2024
OBJECTIVES: This study utilizes [Tc]-methylene diphosphate (MDP) single photon emission computed tomography (SPECT) images as a reference standard to evaluate whether the integration of radiomics features from computed tomography (CT) and machine lea...
Machine learning for predicting cognitive decline within five years in Parkinson's disease: Comparing cognitive assessment scales with DAT SPECT and clinical biomarkers.
PloS one
Jul 17, 2024
OBJECTIVE: Parkinson's disease (PD) is an age-related neurodegenerative condition characterized mostly by motor symptoms. Although a wide range of non-motor symptoms (NMS) are frequently experienced by PD patients. One of the important and common NMS...
An improved method for diagnosis of Parkinson's disease using deep learning models enhanced with metaheuristic algorithm.
BMC medical imaging
Jun 24, 2024
Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) ...
A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.
Scientific reports
Jun 12, 2024
Images obtained from single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) contain noises and artifacts, making cardiovascular disease diagnosis difficult. We developed a deep learning-based diagnosis support system ...
Interpretation of SPECT wall motion with deep learning.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
May 7, 2024
OBJECTIVES: We sought to develop a novel deep learning (DL) workflow to interpret single-photon emission computed tomography (SPECT) wall motion.
Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy.
Annals of neurology
Mar 11, 2024
OBJECTIVE: To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy ...
Clinical Feasibility of Deep Learning-Based Attenuation Correction Models for Tl-201 Myocardial Perfusion SPECT.
Clinical nuclear medicine
Feb 26, 2024
PURPOSE: We aimed to develop deep learning (DL)-based attenuation correction models for Tl-201 myocardial perfusion SPECT (MPS) images and evaluate their clinical feasibility.
Diagnostic accuracy of artificial intelligence-enabled vectorcardiography versus myocardial perfusion SPECT in patients with suspected or known coronary heart disease.
Nuklearmedizin. Nuclear medicine
Feb 20, 2024
AIM: The present study evaluated with myocardial perfusion SPECT (MPS) the diagnostic accuracy of an artificial intelligence-enabled vectorcardiography system (Cardisiography, CSG) for detection of perfusion abnormalities.
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