UrologiQ: AI-based accurate detection, measurement and reporting of stones in CT-KUB scans.

IF 2 2区 医学 Q2 UROLOGY & NEPHROLOGY Urolithiasis Pub Date : 2024-11-28 DOI:10.1007/s00240-024-01671-3
Abhijith Yenikekaluva, Syed Furqan Azeez, Apeksha Sakegaonkar, Aamir Mohammed Shariff, Mehul Wankhede, Shivam Gaikwad, Viharika Pavuluri, S H Anand, Jithunath Madathiparambil Ramanathan
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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.

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UrologiQ:基于人工智能的 CT-KUB 扫描中结石的精确检测、测量和报告。
肾结石疾病在全球范围内越来越常见,其发病率在各个年龄段、种族和地理区域逐年上升。这种急剧增长可能是由于饮食习惯的重大改变。早期准确检测肾结石对于及时干预和预防并发症至关重要。本文将讨论人工智能(AI)在检测肾结石和管理手术治疗中的作用。人工智能技术的最新进展引入了新的工具,改善了医学影像的诊断和分析。与人工方法相比,人工智能可以利用 CT-KUB 图像数据准确检测肾结石的位置,并更有效地测量结石的大小。基于人工智能的检测方法确保了结石识别和测量的更高精度和一致性。这些改进可以帮助医生更有效地制定治疗计划,从而提高患者的治疗成功率。将人工智能整合到肾结石检测和分析中,可显著改善治疗计划和患者管理,从而提高患者疗效和整体医疗质量。
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来源期刊
Urolithiasis
Urolithiasis UROLOGY & NEPHROLOGY-
CiteScore
4.50
自引率
6.50%
发文量
74
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.
期刊最新文献
Correction: Changes in blood gas in supine and prone positions in percutaneous stone surgery: does position have any advantage for hemodynamics? UrologiQ: AI-based accurate detection, measurement and reporting of stones in CT-KUB scans. Exploring the molecular interactions between nephrolithiasis and carotid atherosclerosis: asporin as a potential biomarker. The potential role of Sodium/Glucose Cotransporter 2 inhibitors in the treatment of cystinuria. Identifying therapeutic targets for kidney stone disease through proteome-wide Mendelian randomization and colocalization analysis.
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