AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Imaging, Three-Dimensional

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Real-world application of a 3D deep learning model for detecting and localizing cerebral microbleeds.

Acta neurochirurgica
BACKGROUND: Detection and localization of cerebral microbleeds (CMBs) is crucial for disease diagnosis and treatment planning. However, CMB detection is labor-intensive, time-consuming, and challenging owing to its visual similarity to mimics. This s...

Reliability of brain volume measures of accelerated 3D T1-weighted images with deep learning-based reconstruction.

Neuroradiology
PURPOSE: The time-intensive nature of acquiring 3D T1-weighted MRI and analyzing brain volumetry limits quantitative evaluation of brain atrophy. We explore the feasibility and reliability of deep learning-based accelerated MRI scans for brain volume...

No-reference stereoscopic image quality assessment based on binocular collaboration.

Neural networks : the official journal of the International Neural Network Society
Stereoscopic images typically consist of left and right views along with depth information. Assessing the quality of stereoscopic/3D images (SIQA) is often more complex than that of 2D images due to scene disparities between the left and right views ...

A flexible 2.5D medical image segmentation approach with in-slice and cross-slice attention.

Computers in biology and medicine
Deep learning has become the de facto method for medical image segmentation, with 3D segmentation models excelling in capturing complex 3D structures and 2D models offering high computational efficiency. However, segmenting 2.5D images, characterized...

Smartphone-based scans of palate models of newborns with cleft lip and palate: Outlooks for three-dimensional image capturing and machine learning plate tool.

Orthodontics & craniofacial research
OBJECTIVES: To evaluate the performance of smartphone scanning applications (apps) in acquiring 3D meshes of cleft palate models. Secondarily, to validate a machine learning (ML) tool for computing automated presurgical plate (PSP).

The Promise of Artificial Intelligence in Peyronie's Disease.

Current urology reports
PURPOSE OF REVIEW: The application of artificial intelligence (AI) to enhance clinical decision-making in Peyronie's disease (PD) has generated significant interest. This review explores the current landscape of AI in PD evaluation.

GC-WIR : 3D global coordinate attention wide inverted ResNet network for pulmonary nodules classification.

BMC pulmonary medicine
PURPOSE: Currently, deep learning methods for the classification of benign and malignant lung nodules encounter challenges encompassing intricate and unstable algorithmic models, limited data adaptability, and an abundance of model parameters.To tack...

Automated Deep Learning-Based Finger Joint Segmentation in 3-D Ultrasound Images With Limited Dataset.

Ultrasonic imaging
Ultrasound imaging has shown promise in assessing synovium inflammation associated early stages of rheumatoid arthritis (RA). The precise identification of the synovium and the quantification of inflammation-specific imaging biomarkers is a crucial a...

Rapid CNN-based needle localization for automatic slice alignment in MR-guided interventions using 3D undersampled radial white-marker imaging.

Medical physics
BACKGROUND: In MR-guided in-bore percutaneous needle interventions, typically 2D interactive real-time imaging is used for navigating the needle into the target. Misaligned 2D imaging planes can result in losing visibility of the needle in the 2D ima...

Neural network reconstruction of the left atrium using sparse catheter paths.

International journal of computer assisted radiology and surgery
PURPOSE: Catheter-based radiofrequency ablation for pulmonary vein isolation has become the first line of treatment for atrial fibrillation in recent years. This requires a rather accurate map of the left atrial sub-endocardial surface including the ...