AIMC Topic: Tomography, X-Ray Computed
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Deep-learning-based method for the segmentation of ureter and renal pelvis on non-enhanced CT scans.
Scientific reports
Aug 30, 2024
This study aimed to develop a deep-learning (DL) based method for three-dimensional (3D) segmentation of the upper urinary tract (UUT), including ureter and renal pelvis, on non-enhanced computed tomography (NECT) scans. A total of 150 NECT scans wit...
Enhancing the Diagnostic Accuracy of Sacroiliitis: A Machine Learning Approach Applied to Computed Tomography Imaging.
British journal of hospital medicine (London, England : 2005)
Aug 30, 2024
Sacroiliitis is a challenging condition to diagnose accurately due to the subtle nature of its presentation in imaging studies. This study aims to improve the diagnostic accuracy of sacroiliitis by applying advanced machine learning techniques to co...
Deep learning to predict risk of lateral skull base cerebrospinal fluid leak or encephalocele.
International journal of computer assisted radiology and surgery
Aug 29, 2024
PURPOSE: Skull base features, including increased foramen ovale (FO) cross-sectional area, are associated with lateral skull base spontaneous cerebrospinal fluid (sCSF) leak and encephalocele. Manual measurement requires skill in interpreting imaging...
Use of artificial intelligence algorithms to analyse systemic sclerosis-interstitial lung disease imaging features.
Rheumatology international
Aug 29, 2024
The use of artificial intelligence (AI) in high-resolution computed tomography (HRCT) for diagnosing systemic sclerosis-associated interstitial lung disease (SSc-ILD) is relatively limited. This study aimed to analyse lung HRCT images of patients wit...
Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population.
Thoracic cancer
Aug 29, 2024
BACKGROUND: With the rapid increase of chest computed tomography (CT) images, the workload faced by radiologists has increased dramatically. It is undeniable that the use of artificial intelligence (AI) image-assisted diagnosis system in clinical tre...
A deep convolutional neural network approach using medical image classification.
BMC medical informatics and decision making
Aug 29, 2024
The epidemic diseases such as COVID-19 are rapidly spreading all around the world. The diagnosis of epidemic at initial stage is of high importance to provide medical care to and recovery of infected people as well as protecting the uninfected popula...
Discrepancies in ASPECTS obtained by artificial intelligence and experts: Associated factors and prognostic implications.
European journal of radiology
Aug 28, 2024
PURPOSE: The differences between the Alberta Stroke Program Early CT Score (ASPECTS) obtained by experts and artificial intelligence (AI) software require elucidation. We aimed to characterize the discrepancies between the ASPECTS obtained by AI and ...
Machine Learning Based on Clinical Information and Integrated CT Radiomics to Predict Local Recurrence of Stage Ia Lung Adenocarcinoma after Microwave Ablation.
Journal of vascular and interventional radiology : JVIR
Aug 28, 2024
PURPOSE: To develop and compare 3 different machine learning-based models of clinical information and integrated radiomics features predicting the local recurrence of Stage Ia lung adenocarcinoma after microwave ablation (MWA) for assisting clinical ...
Margins of Excision
Adenocarcinoma of Lung
Ablation Techniques
Neoplasm Staging
Radiographic Image Interpretation, Computer-Assisted
Neoplasm Recurrence, Local
Microwaves
Lung Neoplasms
Treatment Outcome
Predictive Value of Tests
Female
Risk Factors
Aged
Radiomics
Male
Middle Aged
Decision Support Techniques
Humans
Risk Assessment
Retrospective Studies
Machine Learning
Time Factors
Tomography, X-Ray Computed
Clinical Decision-Making
Deep Learning-Based System Combining Chest X-Ray and Computerized Tomography Images for COVID-19 Diagnosis.
British journal of hospital medicine (London, England : 2005)
Aug 27, 2024
The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for accurate and efficient diagnostic methods. This study aims to improve COVID-19 detection by integrating chest X-ray (CXR) and computerized tomography (CT) images using dee...
Predictors of residual tricuspid regurgitation after interventional therapy: an automated deep-learning CT analysis.
Scientific reports
Aug 27, 2024
Computed tomography (CT) is used as a valuable tool for device selection for interventional therapy in tricuspid regurgitation (TR). We aimed to evaluate predictors of TR reduction using CT and automated deep learning algorithms. Patients with severe...
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