AIMC Topic: Radionuclide Imaging
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Artificial Delayed-phase Technetium-99m MIBI Scintigraphy From Early-phase Scintigraphy Improves Identification of Hyperfunctioning Parathyroid Lesions in Patients With Hyperparathyroidism.
Clinical nuclear medicine
Apr 24, 2025
PURPOSE: The aim of this study was to generate and validate artificial delayed-phase technetium-99m methoxyisobutylisonitrile scintigraphy (aMIBI) images from early-phase technetium-99m methoxyisobutylisonitrile scintigraphy (eMIBI) images.
Artificial intelligence-based cardiac transthyretin amyloidosis detection and scoring in scintigraphy imaging: multi-tracer, multi-scanner, and multi-center development and evaluation study.
European journal of nuclear medicine and molecular imaging
Feb 5, 2025
INTRODUCTION: Providing tools for comprehensively evaluating scintigraphy images could enhance transthyretin amyloid cardiomyopathy (ATTR-CM) diagnosis. This study aims to automatically detect and score ATTR-CM in total body scintigraphy images using...
Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
European journal of nuclear medicine and molecular imaging
Jan 29, 2025
PURPOSE: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generat...
Predicting Response to Neuromodulators or Prokinetics in Patients With Suspected Gastroparesis Using Machine Learning: The "BMI, Infectious Prodrome, Delayed GES, and No Diabetes" Model.
Clinical and translational gastroenterology
Sep 1, 2024
INTRODUCTION: Pharmacologic therapies for symptoms of gastroparesis (GP) have limited efficacy, and it is difficult to predict which patients will respond. In this study, we implemented a machine learning model to predict the response to prokinetics ...
A fully automatic deep learning-based method for segmenting regions of interest and predicting renal function in pediatric dynamic renal scintigraphy.
Annals of nuclear medicine
Feb 20, 2024
OBJECTIVE: Accurate delineation of renal regions of interest (ROIs) is critical for the assessment of renal function in pediatric dynamic renal scintigraphy (DRS). The purpose of this study was to develop and evaluate a deep learning (DL) model that ...
Verification of image quality improvement of low-count bone scintigraphy using deep learning.
Radiological physics and technology
Feb 10, 2024
To improve image quality for low-count bone scintigraphy using deep learning and evaluate their clinical applicability. Six hundred patients (training, 500; validation, 50; evaluation, 50) were included in this study. Low-count original images (75%, ...
Deep learning approach for automated segmentation of myocardium using bone scintigraphy single-photon emission computed tomography/computed tomography in patients with suspected cardiac amyloidosis.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Feb 1, 2024
BACKGROUND: We employed deep learning to automatically detect myocardial bone-seeking uptake as a marker of transthyretin cardiac amyloid cardiomyopathy (ATTR-CM) in patients undergoing 99mTc-pyrophosphate (PYP) or hydroxydiphosphonate (HDP) single-p...
Artificial intelligence-based image-domain material decomposition in single-energy computed tomography for head and neck cancer.
International journal of computer assisted radiology and surgery
Jan 14, 2024
PURPOSE: While dual-energy computed tomography (DECT) images provide clinically useful information than single-energy CT (SECT), SECT remains the most widely used CT system globally, and only a few institutions can use DECT. This study aimed to estab...
Development of an individual display optimization system based on deep convolutional neural network transition learning for somatostatin receptor scintigraphy.
Radiological physics and technology
Jan 2, 2024
Somatostatin receptor scintigraphy (SRS) is an essential examination for the diagnosis of neuroendocrine tumors (NETs). This study developed a method to individually optimize the display of whole-body SRS images using a deep convolutional neural netw...
AI in Nuclear Medicine - a review of the current situation.
Nuklearmedizin. Nuclear medicine
Nov 23, 2023
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