AIMC Topic: Urinary Bladder

Clear Filters Showing 11 to 20 of 204 articles

Optimizing bladder magnetic resonance imaging: accelerating scan time and improving image quality through deep learning.

Abdominal radiology (New York)
PURPOSE: To investigate the value of deep learning (DL) in T2-weighted imaging (T2) of the bladder regarding acquisition time (TA), image quality, and diagnostic confidence compared to standard T2-weighted turbo-spin-echo (TSE) imaging (T2).

Machine Vision Augmentation to Detect Detrusor Overactivity in Overactive Bladder: A Frontier of Artificial Intelligence Application in Functional Urology-Proof of Concept Clinical Study.

Neurourology and urodynamics
INTRODUCTION: Overactive bladder (OAB) is a common urological condition with increasing prevalence, especially in an aging population. Diagnosing and treating OAB can be challenging. While urodynamic study (UDS) is useful to confirm involuntary detru...

Quantitative analysis of ureteral jets with dynamic magnetic resonance imaging and a deep-learning approach.

Magnetic resonance imaging
OBJECTIVE: To develop dynamic MRU protocol that focuses on the bladder to capture ureteral jets and to automatically estimate frequency and duration of ureteral jets from the dynamic images.

Unsupervised neural network-based image stitching method for bladder endoscopy.

PloS one
Bladder endoscopy enables the observation of intravesical lesion characteristics, making it an essential tool in urology. Image stitching techniques are commonly employed to expand the field of view of bladder endoscopy. Traditional image stitching m...

Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning.

Cells
The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by ...

A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.

Abdominal radiology (New York)
OBJECTIVE: To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images.

Deep-Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images Using Multiscale and Higher-Order Processing.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Autonomous ultrasound image quality assessment (US-IQA) is a promising tool to aid the interpretation by practicing sonographers and to enable the future robotization of ultrasound procedures. However, autonomous US-IQA has several challenges. Ultras...

Applying Deep-Learning Algorithm Interpreting Kidney, Ureter, and Bladder (KUB) X-Rays to Detect Colon Cancer.

Journal of imaging informatics in medicine
Early screening is crucial in reducing the mortality of colorectal cancer (CRC). Current screening methods, including fecal occult blood tests (FOBT) and colonoscopy, are primarily limited by low patient compliance and the invasive nature of the proc...

A Pre-Voiding Alarm System Using Wearable Ultrasound and Machine Learning Algorithms for Children With Nocturnal Enuresis.

IEEE journal of translational engineering in health and medicine
Nocturnal enuresis is a bothersome condition that affects many children and their caregivers. Post-voiding systems is of little value in training a child into a correct voiding routing while existing pre-voiding systems suffer from several practical ...