ArticleInternational neurourology journal2022
A Study on the Optimal Artificial Intelligence Model for Determination of Urolithiasis.
Article in International neurourology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness.World journal of urology · 2024Pooled it
- From Machine Learning to Generative Artificial Intelligence in Urology: Technological Evolution and Future Perspectives.International neurourology journal · 2026Review
- Automatic kidney stone identification: an adaptive feature-weighted LSTM model based on urine and blood routine analysis.Urolithiasis · 2024Article
- Serious Games as a Therapeutic Tool in Pediatric Urology: A Review of Current Applications and Future Directions.International neurourology journal · 2024Article
- Acoustic emission of kidney stones: a medical adaptation of statistical breakdown mechanisms.Urolithiasis · 2024Article
- The Current State of Artificial Intelligence Application in Urology.International neurourology journal · 2023Article
- Improved Detection of Urolithiasis Using High-Resolution Computed Tomography Images by a Vision Transformer Model.International neurourology journal · 2023Article
- General Overview of Artificial Intelligence for Interstitial Cystitis in Urology.International neurourology journal · 2023Article
- Development and Evaluation of Urolithiasis Detection Technology Based on a Multimethod Algorithm.International neurourology journal · 2023Article
- New Trends in Innovative Technologies Applying Artificial Intelligence to Urinary Diseases.International neurourology journal · 2022Article
- An Artificial Intelligence-assisted Diagnostic System Improves Upper Urine Tract Cytology Diagnosis.In vivo (Athens, Greece)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
purposeThis paper aims to develop a clinical decision support system (CDSS) that can help detect the stone that is most important to the diagnosis of urolithiasis. Among them, especially for the development of artificial intelligence (AI) models that support a final judgment in CDSS, we would like to study the optimal AI model by comparing and evaluating them.
methodsThis paper proposes the optimal ureter stone detection model using various AI technologies. The use of AI technology compares and evaluates methods such as machine learning (support vector machine), deep learning (ResNet-50, Fast R-CNN), and image processing (watershed) to find a more effective method for detecting ureter stones.
resultsThe final value of sensitivity, which is calculated using true positive (TP) and false negative and is a measure of the probability of TP results, showed high recognition accuracy, with an average value of 0.93 for ResNet-50. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery.
conclusionThe general situation in the most effective way to the detection stone can be found. But a variety of variables may be slightly different the difference through the term could tell. Future works, on urological diseases, are diverse and the research will be expanded by customizing AI models specialized for those diseases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.