AIMC Topic: Tomography, X-Ray Computed
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Dual biomarkers CT-based deep learning model incorporating intrathoracic fat for discriminating benign and malignant pulmonary nodules in multi-center cohorts.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Dec 16, 2024
BACKGROUND: Recent studies in the field of lung cancer have emphasized the important role of body composition, particularly fatty tissue, as a prognostic factor. However, there is still a lack of practice in combining fatty tissue to discriminate ben...
CT image super-resolution under the guidance of deep gradient information.
Journal of X-ray science and technology
Dec 15, 2024
Due to the hardware constraints of Computed Tomography (CT) imaging, acquiring high-resolution (HR) CT images in clinical settings poses a significant challenge. In recent years, convolutional neural networks have shown great potential in CT super-re...
Feasibility of Sub-milliSievert Low-dose Computed Tomography with Deep Learning Image Reconstruction in Evaluating Pulmonary Subsolid Nodules: A Prospective Intra-individual Comparison Study.
Academic radiology
Dec 13, 2024
RATIONALE AND OBJECTIVES: To comprehensively assess the feasibility of low-dose computed tomography (LDCT) using deep learning image reconstruction (DLIR) for evaluating pulmonary subsolid nodules, which are challenging due to their susceptibility to...
AI-Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Dec 12, 2024
Aortic stenosis (AS) is the most common valvular heart disease in developed countries. High-fidelity preclinical models can improve AS management by enabling therapeutic innovation, early diagnosis, and tailored treatment planning. However, their use...
Off-console automated artificial intelligence enhanced workflow enables improved emergency department CT capacity.
Emergency radiology
Dec 11, 2024
PURPOSE: Increasing CT capacity to keep pace with rising ED demand is critical. The conventional process has inherent drawbacks. We evaluated an off-console automated AI enhanced workflow which moves all final series creation off-console. We hypothes...
The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review.
European radiology
Dec 10, 2024
AIM: Diagnostic imaging is an integral part of identifying spondyloarthropathies (SpA), yet the interpretation of these images can be challenging. This review evaluated the use of deep learning models to enhance the diagnostic accuracy of SpA imaging...
Spectrum of errors in nodule detection and characterization using machine learning: A pictorial essay.
Current problems in diagnostic radiology
Dec 10, 2024
In academic and research settings, computer-aided nodule detection software has been shown to increase accuracy, efficiency, and throughput. However, radiologists need to be familiar with the spectrum of errors that can occur when these algorithms ar...
Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.
Journal of the American Heart Association
Dec 10, 2024
BACKGROUND: Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage...
Evaluation of the clinical utility of lateral cephalometry reconstructed from computed tomography extracted by artificial intelligence.
Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Dec 9, 2024
This study assessed the accuracy and reliability of artificial intelligence (AI)-reconstructed images of two-dimensional (2D) lateral cephalometric analyses of facial computed tomography (CT) images, which is widely used for the diagnosis of craniofa...
Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning.
Journal of medical engineering & technology
Dec 9, 2024
The conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty issues. This work proposes the automatic detection and classification of COVID-19 by analysing the ...
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