OBJECTIVE: To evaluate the effect of deep learning (DL)-based artificial intelligence (AI) software on the diagnostic performance of radiologists with different experience levels in detecting nigrosome 1 (N1) abnormalities on susceptibility map-weigh...
PURPOSE: Black-blood (BB) magnetic resonance images (MRI) offer superior image contrast for the detection and segmentation of brain metastases (BMs). This study investigated the efficacy and accuracy of deep learning (DL) architectures and post-proce...
PURPOSE: To introduce heatmap-in-heatmap (HIH)-based model for automated diagnosis of foot deformities using weight-bearing foot radiographs, aiming to address the labor-intensive and variable nature of manual diagnosis.
Journal of applied clinical medical physics
Aug 1, 2025
BACKGROUND: Current approaches to lung parcellation utilize established fissures between lobes to provide estimates of lobar volume. However, deep learning segment parcellation provides the ability to better assess regional heterogeneity in ventilati...
BACKGROUND: In ultrasound imaging using plane-wave (PW) techniques, image quality and contrast often suffer, especially when examining anechoic structures. Traditional beamforming methods like Delay-and-Sum or coherent PW compounding face limitations...
Protein science : a publication of the Protein Society
Aug 1, 2025
Since AlphaFold2's rise, many deep learning methods for protein design have emerged. Here, we validate widely used and recognized tools, compare them with first-principle methods, and explore their combinations, focusing on their effectiveness in pro...
Predicting brain age from T1-weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep learning models have shown success in reducing the mean absolute error (MAE) of predicted brain age, concerns abou...
This narrative review presents deep learning-based strategies for generating synthetic 3D CT-like images from biplanar or multiplanar 2D X-ray data. Current limitations of conventional CT imaging are discussed, hence emphasizing the potential of synt...
Neuropathology and applied neurobiology
Aug 1, 2025
Gliomas, with their intricate and aggressive nature, call for a detailed visualisation of their vasculature. Traditional 2D imaging often overlooks the spatial heterogeneity of tumours. Our study overcomes this by combining tissue clearing, 3D-confoc...
International journal of neural systems
Aug 1, 2025
The diagnosis of autism spectrum disorder (ASD) is often hampered by its heterogeneity and reliance on time-consuming behavioral assessments. Automated neuroimaging-based diagnostic tools offer a promising alternative, but multi-site data integration...
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