AIMC Topic: Tomography, X-Ray Computed

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Enhancing facial feature de-identification in multiframe brain images: A generative adversarial network approach.

Progress in brain research
The collection of head images for public datasets in the field of brain science has grown remarkably in recent years, underscoring the need for robust de-identification methods to adhere with privacy regulations. This paper elucidates a novel deep le...

Assessing GPT-4 multimodal performance in radiological image analysis.

European radiology
OBJECTIVES: This study aims to assess the performance of a multimodal artificial intelligence (AI) model capable of analyzing both images and textual data (GPT-4V), in interpreting radiological images. It focuses on a range of modalities, anatomical ...

Deep-learning-based method for the segmentation of ureter and renal pelvis on non-enhanced CT scans.

Scientific reports
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)
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
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
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
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
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
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 ...