AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Detection of COVID-19, lung opacity, and viral pneumonia via X-ray using machine learning and deep learning.
Computers in biology and medicine
Jun 1, 2025
The COVID-19 pandemic has significantly strained healthcare systems, highlighting the need for early diagnosis to isolate positive cases and prevent the spread. This study combines machine learning, deep learning, and transfer learning techniques to ...
Comparative Performance of Anthropic Claude and OpenAI GPT Models in Basic Radiological Imaging Tasks.
Journal of medical imaging and radiation oncology
Jun 1, 2025
BACKGROUND: Publicly available artificial intelligence (AI) Vision Language Models (VLMs) are constantly improving. The advent of vision capabilities on these models could enhance radiology workflows. Evaluating their performance in radiological imag...
Accuracy of a deep neural network for automated pulmonary embolism detection on dedicated CT pulmonary angiograms.
European journal of radiology
Jun 1, 2025
PURPOSE: To assess the performance of a Deep Neural Network (DNN)-based prototype algorithm for automated PE detection on CTPA scans.
Algorithms
Retrospective Studies
Radiographic Image Interpretation, Computer-Assisted
Female
Pulmonary Embolism
Reproducibility of Results
Computed Tomography Angiography
Sensitivity and Specificity
Humans
Pulmonary Artery
Male
Neural Networks, Computer
Aged
Middle Aged
Aged, 80 and over
Deep Learning
Adult
Predicting hepatocellular carcinoma response to TACE: A machine learning study based on 2.5D CT imaging and deep features analysis.
European journal of radiology
Jun 1, 2025
OBJECTIVES: Prior to the commencement of treatment, it is essential to establish an objective method for accurately predicting the prognosis of patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE). In this st...
Does the deep learning-based iterative reconstruction affect the measuring accuracy of bone mineral density in low-dose chest CT?
The British journal of radiology
Jun 1, 2025
OBJECTIVES: To investigate the impacts of a deep learning-based iterative reconstruction algorithm on image quality and measuring accuracy of bone mineral density (BMD) in low-dose chest CT.
Deep learning reconstruction improves computer-aided pulmonary nodule detection and measurement accuracy for ultra-low-dose chest CT.
BMC medical imaging
May 30, 2025
PURPOSE: To compare the image quality and pulmonary nodule detectability and measurement accuracy between deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) of chest ultra-low-dose CT (ULDCT).
Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images.
BMC musculoskeletal disorders
May 20, 2025
OBJECTIVE: The aim of this study was to develop a hybrid diagnostic framework integrating radiomic and deep features for accurate and reproducible detection and classification of wrist fractures using X-ray images.
A clinically applicable AI system for detection and diagnosis of bone metastases using CT scans.
Nature communications
May 13, 2025
Manual interpretation of CT images for bone metastasis (BM) detection in primary cancer remains challenging. We present an automated Bone Lesion Detection System (BLDS) developed using CT scans from 2518 patients (9177 BMs; 12,824 non-BM lesions) acr...
Assessment of Image Quality of Coronary Computed Tomography Angiography in Obese Patients by Comparing Deep Learning Image Reconstruction With Adaptive Statistical Iterative Reconstruction Veo.
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: The aim of the study was to evaluate the image quality of coronary computed tomography (CT) angiography (CCTA) in obese patients by using deep learning image reconstruction (DLIR) in comparison with adaptive statistical iterative reconstru...
In Vitro Study of the Precision and Accuracy of Measurement of the Vascular Inner Diameter on Computed Tomography Angiography Using Deep Learning Image Reconstruction: Comparison With Filtered Back Projection and Iterative Reconstruction.
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: This study aimed to compare the performance of deep learning image reconstruction (DLIR) with that of standard filtered back projection (FBP) and adaptive statistical iterative reconstruction V (ASiR-V) for measurement of the vascular diam...
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