Latest AI and machine learning research in nuclear medicine for healthcare professionals.
BACKGROUND: Prostate cancer is the second most common cancer in men, with rising mortality rates necessitating precise risk stratification. High-invasive biological features-specifically International Society of Urological Pathology (ISUP) grade, extracapsular extension (EPE), and positive surgical margins (PSM)-are critical for guiding treatment but are difficult to detect due to tumor heterogene...
RATIONALE AND OBJECTIVES: PD-L1 expression is a critical biomarker in guiding immunotherapy for gastric cancer (GC). This study aims to investigate the value of deep learning analysis based on dual-energy CT-derived iodine map for predicting the level of PD-L1 expression in GC. METHODS: A total of 267 GC patients who underwent gastrectomy and preoperative dual-energy CT from multiple centers were ...
Pleural diseases pose a significant burden on healthcare systems due to diagnostic challenges and high costs. Artificial intelligence (AI) has the pot...
OBJECTIVES: This study aims to develop a deep learning model to assist physicians in accurately classifying negative, equivocal, and positive β-amyloi...
The incorporation of a trifluoromethyl (-CF3) group into organic frameworks profoundly influences their physicochemical and biological properties, mak...
The application of machine learning (ML) and artificial intelligence (AI) algorithms in medical imaging is an emerging area of interest, particularly ...
Positron Emission Tomography (PET) is important for breast cancer diagnosis and monitoring, but high costs restrict access. Dual-panel scanners can re...
Positron emission tomography (PET)/computed tomography (CT) for myocardial perfusion imaging (MPI) provides multiple imaging biomarkers, often evaluat...
Quality assessment of crude palm oil remains a critical challenge globally, particularly in resource-poor areas where traditional methods are time-con...
Quantitative PET imaging requires accurate attenuation and scatter correction (ASC), but the standard CT-based method introduces additional radiation ...
OBJECTIVE: To identify pre-treatment determinants of hypothyroidism and decision regret (DR) following radioiodine (RAI) therapy in Graves' disease (G...
PURPOSE: This study aims to develop and validate an interpretable machine learning model that integrates clinical data, radiomics, and deep learning (...
PURPOSE: There has been significant progress in detecting Alzheimer's disease (AD) using retinal imaging. We developed an ensemble learning-based deep...
OBJECTIVES: Amyloid-β (Aβ) PET is crucial for diagnosing and monitoring Alzheimer's disease (AD), but its high cost and radiation exposure limit its u...
Assessing small-molecule blood-brain barrier permeability is laborious, yet critical in drug development. Quantitative prediction models are hindered ...
PURPOSE: This study aimed to develop deep learning (DL) models for CT-free attenuation correction and Monte Carlo-based scatter correction in 99mTc-ma...
BACKGROUND: Accurate preoperative evaluation of rectal cancer is essential for staging and treatment planning. Low-energy virtual monoenergetic imagin...
PURPOSE: To assess machine learning (ML) classifiers trained on harmonised multicentre ¹²³I-mIBG planar scintigraphy for differentiating Parkinson's d...
OBJECTIVE: To construct and validate a model for predicting lymph node metastasis (LNMs) of gastric cancer (GC) based on 18F-FDG PET/CT multi-paramete...
In medical image analysis, regression plays a critical role in computer-aided diagnosis. It enables quantitative measurements such as age prediction f...