AIMC Topic: Metals

Clear Filters Showing 11 to 20 of 126 articles

Semi-supervised spatial-frequency transformer for metal artifact reduction in maxillofacial CT and evaluation with intraoral scan.

European journal of radiology
PURPOSE: To develop a semi-supervised domain adaptation technique for metal artifact reduction with a spatial-frequency transformer (SFTrans) model (Semi-SFTrans), and to quantitatively compare its performance with supervised models (Sup-SFTrans and ...

Assessing potential toxic metal threats in tea growing soils of India with soil health indices and machine learning technologies.

Environmental monitoring and assessment
This study explores the impact of potentially toxic metals (PTMs) contamination in Indian tea-growing soils on ecosystems, soil quality, and human health using machine learning and statistical analysis. A total of 148 surface soil samples were collec...

Metal Suppression Magnetic Resonance Imaging Techniques in Orthopaedic and Spine Surgery.

The Journal of the American Academy of Orthopaedic Surgeons
Implantation of metallic instrumentation is the mainstay of a variety of orthopaedic and spine surgeries. Postoperatively, imaging of the soft tissues around these implants is commonly required to assess for persistent, recurrent, and/or new patholog...

Self-supervised U-transformer network with mask reconstruction for metal artifact reduction.

Physics in medicine and biology
. Metal artifacts severely damaged human tissue information from the computed tomography (CT) image, posing significant challenges to disease diagnosis. Deep learning has been widely explored for the metal artifact reduction (MAR) task. Nevertheless,...

Construction of multi-metal interspecies correlation estimation models based on typical soil scenarios.

Environmental research
The ecological risk assessment of metals in soils is essential for soil pollution management. However, regional soil heterogeneity and species diversity need to be considered when making these assessments. Therefore, an interspecies correlation estim...

High-Throughput Prediction of Metal-Embedded Complex Properties with a New GNN-Based Metal Attention Framework.

Journal of chemical information and modeling
Metal-embedded complexes (MECs), including transition metal complexes (TMCs) and metal-organic frameworks (MOFs), are important in catalysis, materials science, and molecular devices due to their unique metal atom centrality and complex coordination ...

Does Deep Learning Reconstruction Improve Ureteral Stone Detection and Subjective Image Quality in the CT Images of Patients with Metal Hardware?

Journal of endourology
Diagnosing ureteral stones with low-dose CT in patients with metal hardware can be challenging because of image noise. The purpose of this study was to compare ureteral stone detection and image quality of low-dose and conventional CT scans with and...

Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence.

Molecules and cells
Metal coordination is essential for structural/catalytic functions of metalloproteins that mediate a wide range of biological processes in living organisms. Advances in bioinformatics have significantly enhanced our understanding of metal-binding sit...

Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.

Frontiers in immunology
INTRODUCTION: Type 2 diabetes (T2D) is a complex metabolic disorder with significant global health implications. Understanding the molecular mechanisms underlying T2D is crucial for developing effective therapeutic strategies. This study employs sing...

DisDock: A Deep Learning Method for Metal Ion-Protein Redocking.

Proteins
The structures of metalloproteins are essential for comprehending their functions and interactions. The breakthrough of AlphaFold has made it possible to predict protein structures with experimental accuracy. However, the type of metal ion that a met...