Phylogenies contain a wealth of information about the evolutionary history and process that gave rise to the diversity of life. This information can be extracted by fitting phylogenetic models to trees. However, many realistic phylogenetic models lac...
UNLABELLED: Precision oncology is becoming increasingly integral to clinical practice, demonstrating notable improvements in treatment outcomes. Whereas molecular data provide comprehensive insights, obtaining such data remains costly and time-consum...
UNLABELLED: Tertiary lymphoid structures (TLS) are critical components of the tumor microenvironment in gastric cancer, but clinical assessment of TLSs is challenging. The development of automated annotation tools for histopathologic slide analysis c...
BACKGROUND: Intravascular ultrasound (IVUS)-based tissue characterization has been used to detect vulnerable plaque or lipid-rich plaque (LRP). Recently, advancements in artificial intelligence (AI) technology have enabled automated coronary arterial...
BACKGROUND: Deep learning (DL) models are effective pre-screening tools for detecting mismatch repair deficiency (dMMR) in colorectal carcinoma (CRC). These models have been trained and validated on large cohorts from the Northern Hemisphere, without...
AJNR. American journal of neuroradiology
Dec 4, 2025
BACKGROUND AND PURPOSE: Distinguishing GM from WM is essential for CT of the brain. The recently established photon-counting detector (PCD)-CT technology uses a novel detection technique that might allow more precise measurement of tissue attenuation...
AJNR. American journal of neuroradiology
Dec 4, 2025
BACKGROUND AND PURPOSE: Identifying amyloid-beta (Aβ)-positive patients is essential for Alzheimer disease clinical trials and disease-modifying treatments but currently requires PET or CSF sampling. Previous MRI-based deep learning models using only...
OBJECTIVE: This study aims to determine the diagnostic performance of sub-5-min combined sixfold parallel imaging (PIx3)-simultaneous multislice (SMSx2)-accelerated deep learning (DL) super-resolution 3T knee MRI in children and adolescents.
OBJECTIVE: The purpose of this study is to compare radiologists' breast cancer screening performance before and after the implementation of an artificial intelligence (AI) detection system for digital breast tomosynthesis (DBT).
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