Abhijith Yenikekaluva, Syed Furqan Azeez, Apeksha Sakegaonkar, Aamir Mohammed Shariff, Mehul Wankhede, Shivam Gaikwad, Viharika Pavuluri, S H Anand, Jithunath Madathiparambil Ramanathan
{"title":"UrologiQ: AI-based accurate detection, measurement and reporting of stones in CT-KUB scans.","authors":"Abhijith Yenikekaluva, Syed Furqan Azeez, Apeksha Sakegaonkar, Aamir Mohammed Shariff, Mehul Wankhede, Shivam Gaikwad, Viharika Pavuluri, S H Anand, Jithunath Madathiparambil Ramanathan","doi":"10.1007/s00240-024-01671-3","DOIUrl":null,"url":null,"abstract":"<p><p>Kidney stone disease is becoming increasingly common worldwide, with its prevalence increasing annually across all age groups, races, and geographic regions. This sharp increase may be due to significant changes in dietary habits. Early and accurate detection of kidney stones is crucial for timely intervention and prevention of complications. This article discusses the role of artificial intelligence (AI) in detecting kidney stones and managing surgical treatments. Recent advances in AI techniques have introduced new tools that improve the diagnosis and analysis of medical images. AI can use CT-KUB image data to accurately detect the location of kidney stones and measure their size more efficiently than manual methods. AI-based detection methods ensure greater precision and consistency in stone identification and measurement. These improvements can help doctors plan treatments more effectively, resulting in a higher success rate for patients. Integrating AI into kidney stone detection and analysis significantly improves treatment planning and patient management, leading to better patient outcomes and overall quality of healthcare.</p>","PeriodicalId":23411,"journal":{"name":"Urolithiasis","volume":"52 1","pages":"170"},"PeriodicalIF":2.0000,"publicationDate":"2024-11-28","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/s00240-024-01671-3","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 stone disease is becoming increasingly common worldwide, with its prevalence increasing annually across all age groups, races, and geographic regions. This sharp increase may be due to significant changes in dietary habits. Early and accurate detection of kidney stones is crucial for timely intervention and prevention of complications. This article discusses the role of artificial intelligence (AI) in detecting kidney stones and managing surgical treatments. Recent advances in AI techniques have introduced new tools that improve the diagnosis and analysis of medical images. AI can use CT-KUB image data to accurately detect the location of kidney stones and measure their size more efficiently than manual methods. AI-based detection methods ensure greater precision and consistency in stone identification and measurement. These improvements can help doctors plan treatments more effectively, resulting in a higher success rate for patients. Integrating AI into kidney stone detection and analysis significantly improves treatment planning and patient management, leading to better patient outcomes and overall quality of healthcare.
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.