AIMC Topic: Urinary Bladder

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Explainable and likelihood aware AI framework for MRI-based pixel-level bladder tumour prediction.

Scientific reports
Bladder tumours (BTs) pose significant clinical challenges due to their high recurrence rates and risk of progression to invasive malignancies, which emphasises the need for early and accurate detection. Magnetic resonance imaging (MRI), with its sup...

Cortical modulation through robotic gait training with motor imagery brain-computer interface enhances bladder function in individuals with spinal cord injury.

Scientific reports
Neurogenic bladder (NB) dysfunction in individuals with complete spinal cord injury (SCI) is a condition that significantly affects quality of life. Despite the prevalence of interventions, there is a substantial gap in effective treatments for this ...

Efficient Compression of Mass Spectrometry Images via Contrastive Learning-Based Encoding.

Analytical chemistry
In this study, we introduce a novel encoding algorithm utilizing contrastive learning to address the substantial data size challenges inherent in mass spectrometry imaging. Our algorithm compresses MSI data into fixed-length vectors, significantly re...

Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla.

BMC medical imaging
OBJECTIVE: This prospective study aimed to assess the feasibility of a half-Fourier single-shot turbo spin echo sequence (HASTE) with deep learning (DL) reconstruction for ultrafast imaging of the bladder with reduced susceptibility to motion artifac...

Artificial intelligence in muscle-invasive bladder cancer: opportunities, challenges, and clinical impact.

Current opinion in urology
PURPOSE OF REVIEW: Muscle-invasive bladder cancer (MIBC) represents an aggressive malignancy with significant morbidity and mortality. Recent advances in artificial intelligence (AI) offer promising opportunities to enhance patient care across the en...

Decision support using machine learning for predicting adequate bladder filling in prostate radiotherapy: a feasibility study.

Radiological physics and technology
This study aimed to develop a model for predicting the bladder volume ratio between daily CBCT and CT to determine adequate bladder filling in patients undergoing treatment for prostate cancer with external beam radiation therapy (EBRT). The model wa...

Characteristic genes and immune landscape of interstitial cystitis.

PloS one
BACKGROUND: Interstitial cystitis (IC) was still a disease with the exclusive diagnosis and lacked an effective gold standard. It was of great significance to find diagnostic markers for IC. Our study was aimed to screen characteristic genes via mach...

Using deep learning generated CBCT contours for online dose assessment of prostate SABR treatments.

Journal of applied clinical medical physics
Prostate Stereotactic Ablative Body Radiotherapy (SABR) is an ultra-hypofractionated treatment where small setup errors can lead to higher doses to organs at risk (OARs). Although bowel and bladder preparation protocols reduce inter-fraction variabil...

Machine learning modeling and multi objective optimization of artificial detrusor.

Scientific reports
To address the problem of obtaining optimal design parameters for existing artificial detrusors using single-objective optimization methods, this research proposed a machine learning-based artificial detrusor modeling and multi-objective optimization...

Reduction of the planning target volume with daily online adaptive radiotherapy in bladder cancer.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
INTRODUCTION: External radiation therapy for bladder cancer requires large planning target volumes (PTVs) due to the daily anatomy of the bladder. Online adaptive radiotherapy (oART) can reduce the PTV by considering daily anatomical changes.