AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Jan 31, 2024
This study aimed to investigate whether deep-learning reconstruction (DLR) improves interobserver agreement in the evaluation of honeycombing for patients with interstitial lung disease (ILD) who underwent high-resolution computed tomography (CT) co...
Improved image quality in CT pulmonary angiography using deep learning-based image reconstruction.
Scientific reports
Jan 30, 2024
We investigated the effect of deep learning-based image reconstruction (DLIR) compared to iterative reconstruction on image quality in CT pulmonary angiography (CTPA) for suspected pulmonary embolism (PE). For 220 patients with suspected PE, CTPA stu...
Deep Learning-Based Reconstruction Improves the Image Quality of Low-Dose CT Colonography.
Academic radiology
Jan 29, 2024
RATIONALE AND OBJECTIVES: To evaluate the image quality of low-dose CT colonography (CTC) using deep learning-based reconstruction (DLR) compared to iterative reconstruction (IR).
Analysis of tricuspid annulus dimensions and RCA-proximity with artificial intelligence-based software for procedural planning of percutaneous tricuspid annuloplasty.
Journal of cardiovascular computed tomography
Jan 29, 2024
Female
Aged
Male
Middle Aged
Treatment Outcome
Predictive Value of Tests
Coronary Angiography
Radiographic Image Interpretation, Computer-Assisted
Computed Tomography Angiography
Cardiac Valve Annuloplasty
Tricuspid Valve
Heart Valve Prosthesis Implantation
Humans
Tricuspid Valve Insufficiency
Artificial Intelligence
Cardiac Catheterization
Software
Clinical Decision-Making
Improving image quality of triple-low-protocol renal artery CT angiography with deep-learning image reconstruction: a comparative study with standard-dose single-energy and dual-energy CT with adaptive statistical iterative reconstruction.
Clinical radiology
Jan 23, 2024
AIM: To investigate the improvement in image quality of triple-low-protocol (low radiation, low contrast medium dose, low injection speed) renal artery computed tomography (CT) angiography (RACTA) using deep-learning image reconstruction (DLIR), in c...
Determination of growth and developmental stages in hand-wrist radiographs : Can fractal analysis in combination with artificial intelligence be used?
Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie
Jan 22, 2024
PURPOSE: The goal of this work was to assess the classification of maturation stage using artificial intelligence (AI) classifiers.
Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.
Academic radiology
Jan 22, 2024
RATIONALE AND OBJECTIVES: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop...
CT image denoising methods for image quality improvement and radiation dose reduction.
Journal of applied clinical medical physics
Jan 19, 2024
With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in lo...
MI-DenseCFNet: deep learning-based multimodal diagnosis models for Aureus and Aspergillus pneumonia.
European radiology
Jan 17, 2024
OBJECTIVE: To build and merge a diagnostic model called multi-input DenseNet fused with clinical features (MI-DenseCFNet) for discriminating between Staphylococcus aureus pneumonia (SAP) and Aspergillus pneumonia (ASP) and to evaluate the significant...
Artificial Intelligence for Breast Cancer Detection on Mammography: Factors Related to Cancer Detection.
Academic radiology
Jan 11, 2024
RATIONALE AND OBJECTIVES: Little is known about the factors affecting the Artificial Intelligence (AI) software performance on mammography for breast cancer detection. This study was to identify factors associated with abnormality scores assigned by ...
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