PURPOSE: This study compared field-of-view (FOV) optimized and constrained undistorted single-shot diffusion-weighted imaging (FOCUS DWI) with deep-learning-based reconstruction (DLR) to conventional DWI for breast imaging.
Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Oct 30, 2024
OBJECTIVE: Glioblastoma (GBM), one of the most common brain tumors, is known for its low survival rates and poor treatment responses. This study aims to create a robust predictive model that integrates multiple feature types, including clinical data,...
RATIONALE AND OBJECTIVES: To develop and evaluate an AI algorithm that detects breast cancer in MRI scans up to one year before radiologists typically identify it, potentially enhancing early detection in high-risk women.
PURPOSE: To train and validate machine learning-derived clinical decision algorithm (CDA) for the diagnosis of hyperfunctioning parathyroid glands using preoperative variables to facilitate surgical planning.
BACKGROUND: Recently developed artificial intelligence-based coronary angiography (AI-QCA, fully automated) provides real-time, objective, and reproducible quantitative analysis of coronary angiography without requiring additional time or labor.
BACKGROUND: Parkinson's disease (PD) is a major neurodegenerative disorder in Middle-aged and elderly people.There is a pressing need for effective predictive models, particularly in chinese population.
Acute ischemic stroke (AIS) remains a global health challenge, leading to long-term functional disabilities without timely intervention. Spatio-temporal (4D) Computed Tomography Perfusion (CTP) imaging is crucial for diagnosing and treating AIS due t...
BACKGROUND: With the rising incidence of pulmonary nodules (PNs), lung adenocarcinoma in situ (AIS) is a critical early stage of lung cancer, necessitating accurate diagnosis for early intervention. This study applies artificial intelligence (AI) for...
International journal of molecular sciences
Oct 30, 2024
Reliable predictors of long-term all-cause mortality are needed for middle-aged and older populations. Previous metabolomics mortality studies have limitations: a low number of participants and metabolites measured, measurements mainly using nuclear ...
PURPOSE: To establish a deep learning artificial intelligence model to predict the risk of long-term fellow eye neovascularization in unilateral type 3 macular neovascularization (MNV).
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