This study shows the feasibility of utilizing deep learning to assess whether urinary tract stones are uric acid stones through CT scans, blood, and urine tests. It can serve as a supplementary tool for traditional stone composition analysis, offering decision support for urologists and enhancing the effectiveness of diagnosis and treatment.
Keyphrases
- urinary tract
- uric acid
- deep learning
- dual energy
- computed tomography
- metabolic syndrome
- contrast enhanced
- image quality
- artificial intelligence
- convolutional neural network
- randomized controlled trial
- positron emission tomography
- high resolution
- machine learning
- systematic review
- magnetic resonance imaging
- editorial comment
- real time pcr
- label free
- loop mediated isothermal amplification
- data analysis