Radiology

Nuclear Medicine

Latest AI and machine learning research in nuclear medicine for healthcare professionals.

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Deep learning-based approach for acquisition time reduction in ventilation SPECT in patients after lung transplantation.

We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunct...

Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

BACKGROUND: Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enha...

A support vector machine-based approach to guide the selection of a pseudo-reference region for brain PET quantification.

A Support Vector Machine (SVM) based approach was developed to identify a pseudo-reference region fo...

Application of a Deep Learning-Based Contrast-Boosting Algorithm to Low-Dose Computed Tomography Pulmonary Angiography With Reduced Iodine Load.

OBJECTIVE: The aim of this study was to assess the effectiveness of a deep learning-based image cont...

Total-Body PET/CT: A Role of Artificial Intelligence?

The purpose of this paper is to provide an overview of the cutting-edge applications of artificial i...

Automated deep learning segmentation of cardiac inflammatory FDG PET.

BACKGROUND: Fluorodeoxyglucose positron emission tomography (FDG PET) with suppression of myocardial...

Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images.

Medical image classification using convolutional neural networks (CNNs) is promising but often requi...

Deep learning-based binary classification of beta-amyloid plaques using 18 F florapronol PET.

PURPOSE: This study aimed to investigate a deep learning model to classify amyloid plaque deposition...

Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks.

The standard method for identifying active Brown Adipose Tissue (BAT) is [F]-Fluorodeoxyglucose ([F]...

Generative AI and large language models in nuclear medicine: current status and future prospects.

This review explores the potential applications of Large Language Models (LLMs) in nuclear medicine,...

Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients' electrocardiograms.

We develop a machine learning (ML) model using electrocardiography (ECG) to predict myocardial blood...

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.

BACKGROUND: Natural language processing (NLP) techniques can be used to analyze large amounts of ele...

Comparative evaluation of machine learning models in predicting overall survival for nasopharyngeal carcinoma using F-FDG PET-CT parameters.

PURPOSE: The objective of this study is to assess the prognostic efficacy of F-fluorodeoxyglucose (F...

Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review.

This systematic review aimed to evaluate the potential of deep learning algorithms for converting lo...

Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer withF-FDG PET/CT images.

. Approximately 57% of non-small cell lung cancer (NSCLC) patients face a 20% risk of brain metastas...

Applying deep learning-based ensemble model to [F]-FDG-PET-radiomic features for differentiating benign from malignant parotid gland diseases.

OBJECTIVES: To develop and identify machine learning (ML) models using pretreatment 2-deoxy-2-[F]flu...

Clinical performance of deep learning-enhanced ultrafast whole-body scintigraphy in patients with suspected malignancy.

BACKGROUND: To evaluate the clinical performance of two deep learning methods, one utilizing real cl...

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