{"title":"Evaluating the effectiveness of AI-powered UrologiQ's in accurately measuring kidney stone volume in urolithiasis patients.","authors":"Abhijith Yenikekaluva, Madhu Sudan Agrawal, Jithunath Madathiparambil Ramanathan, Syed Furqan Azeez, Apeksha Sakegaonkar, Aamir Mohammed Shariff","doi":"10.1007/s00240-024-01659-z","DOIUrl":null,"url":null,"abstract":"<p><p>Kidney stones and urolithiasis are kidney diseases that have a significant impact on health and well-being, and their incidence is increasing annually owing to factors such as age, sex, ethnicity, and geographical location. Accurate identification and volume measurement of kidney stones are critical for determining the appropriate surgical approach, as timely and precise treatment is essential to prevent complications and ensure successful outcomes. Larger stones often require more invasive procedures, and precise volume measurements are essential for effective surgical planning and patient outcomes. This study aimed to compare the ability of artificial intelligence (AI) to detect and measure kidney stone volume via CT-KUB images. CT KUB imaging data were analyzed to determine the effectiveness of AI in identifying the volume of kidney stones. The results were compared with measurements taken by radiologists. Compared with radiologists, the AI had greater accuracy, efficiency, and consistency in measuring kidney stone volume. The AI calculates the volume of kidney stones with an average difference of 80% compared with the volumes calculated by radiologists, highlighting a significant discrepancy that is critical for accurate surgical planning. The results suggest that artificial intelligence (AI) outperforms radiologists' manual calculations in measuring kidney stone volume. By integrating AI with kidney stone detection and treatment, there is potential for greater diagnostic precision and treatment effectiveness, which could ultimately improve patient outcomes.</p>","PeriodicalId":23411,"journal":{"name":"Urolithiasis","volume":"52 1","pages":"158"},"PeriodicalIF":2.0000,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Urolithiasis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00240-024-01659-z","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Kidney stones and urolithiasis are kidney diseases that have a significant impact on health and well-being, and their incidence is increasing annually owing to factors such as age, sex, ethnicity, and geographical location. Accurate identification and volume measurement of kidney stones are critical for determining the appropriate surgical approach, as timely and precise treatment is essential to prevent complications and ensure successful outcomes. Larger stones often require more invasive procedures, and precise volume measurements are essential for effective surgical planning and patient outcomes. This study aimed to compare the ability of artificial intelligence (AI) to detect and measure kidney stone volume via CT-KUB images. CT KUB imaging data were analyzed to determine the effectiveness of AI in identifying the volume of kidney stones. The results were compared with measurements taken by radiologists. Compared with radiologists, the AI had greater accuracy, efficiency, and consistency in measuring kidney stone volume. The AI calculates the volume of kidney stones with an average difference of 80% compared with the volumes calculated by radiologists, highlighting a significant discrepancy that is critical for accurate surgical planning. The results suggest that artificial intelligence (AI) outperforms radiologists' manual calculations in measuring kidney stone volume. By integrating AI with kidney stone detection and treatment, there is potential for greater diagnostic precision and treatment effectiveness, which could ultimately improve patient outcomes.
期刊介绍：
Official Journal of the International Urolithiasis Society
The journal aims to publish original articles in the fields of clinical and experimental investigation only within the sphere of urolithiasis and its related areas of research. The journal covers all aspects of urolithiasis research including the diagnosis, epidemiology, pathogenesis, genetics, clinical biochemistry, open and non-invasive surgical intervention, nephrological investigation, chemistry and prophylaxis of the disorder. The Editor welcomes contributions on topics of interest to urologists, nephrologists, radiologists, clinical biochemists, epidemiologists, nutritionists, basic scientists and nurses working in that field.
Contributions may be submitted as full-length articles or as rapid communications in the form of Letters to the Editor. Articles should be original and should contain important new findings from carefully conducted studies designed to produce statistically significant data. Please note that we no longer publish articles classified as Case Reports. Editorials and review articles may be published by invitation from the Editorial Board. All submissions are peer-reviewed. Through an electronic system for the submission and review of manuscripts, the Editor and Associate Editors aim to make publication accessible as quickly as possible to a large number of readers throughout the world.
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