Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 522
- 试验地点
- 1
- 主要终点
- Comparison of stone diameter from manual segmentation with radiology report
研究概览
简要总结
Kidney stone disease causes significant morbidity, and stones obstructing the ureter can have serious consequences. Imaging diagnostics with computed tomography (CT) are crucial for diagnosis, treatment selection, and follow-up. Segmentation of CT images can provide objective data on stone burden and signs of obstruction. Artificial intelligence (AI) can automate such segmentation but can also be used for the diagnosis of stone disease and obstruction.
In this project, the aim is to investigate if:
Manual segmentation of CT scans can provide more accurate information about kidney stone disease compared to conventional interpretation.
AI segmentation yields valid results compared to manual segmentation. AI can detect ureteral stones and obstruction or predict spontaneous passage.
详细描述
Background:
Goals and Objectives:
The project aims to contribute to personalized and improved treatment and follow-up of patients with kidney stones using radiomics and the development of an artificial intelligence tool for CT examination assessment. The objectives are to assess:
- Whether manual segmentation of CT images of the urinary tract provides equivalent or more accurate information about kidney stone disease compared to conventional interpretation and reporting.
- Whether segmentation performed with AI yields valid results compared to manual segmentation.
- Whether AI can detect ureteral stones and obstruction and/or predict spontaneous passage of stones.
Method:
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Referral for CT due to episode of renal colic and clinical suspicion of urinary stone disease or
- •Referral for CT due to new episode of pain in patient with known urinary stone disease
- •Age ≥ 18 years
排除标准
- •Referral for control CT of asymptomatic patients with known urinary stone disease
- •Referral for control CT after treatment
- •Referral for control CT for spontaneous passage of stone.
- •Lack of informed consent for any reason.
结局指标
主要结局
Comparison of stone diameter from manual segmentation with radiology report
时间窗: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Stone diameter (in mm) compared between manual segmentation and radiology report (paired t-test or wilcoxon rank sum test if non-normally distributed data)
Comparison of AI-segmentation of stones (DICE-score) with manual segmentation
时间窗: At time of CT examination (inclusion and follow up - expected average 12 weeks)
DICE-score for AI-segmentation of stones, compared to manual segmenation (gold standard)
Prospective performance (diagnostic accuracy) of AI detection of ureteral stone (compared to radiology report (gold standard)
时间窗: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Comparison of differences in dicotomous proportions in paired data according to Newcombe
次要结局
- Comparison of distention of renal pelvis from manual segmentation with radiology report(At time of CT examination (inclusion and follow up - expected average 12 weeks))
- Comparison of AI-segmentation of renal pelvis (diagnostic accuracy) with manual segmentation(At time of CT examination (inclusion and follow up - expected average 12 weeks))
- Comparison of AI-segmentation of renal parenchyma (diagnostic accuracy) with manual segmentation(At time of CT examination (inclusion and follow up - expected average 12 weeks))
- Comparison of AI-segmentation of stones (diagnostic accuracy) with manual segmentation(At time of CT examination (inclusion and follow up - expected average 12 weeks))
- Comparison of stone density from manual segmentation with radiology report(At time of CT examination (inclusion and follow up - expected average 12 weeks))
- Comparison of AI-segmentation of stones (Hausdorff distance) with manual segmentation(At time of CT examination (inclusion and follow up - expected average 12 weeks))
- Comparison of AI-segmentation of renal pelvis (Hausdorff distance) with manual segmentation(At time of CT examination (inclusion and follow up - expected average 12 weeks))
- Comparison of AI-segmentation of renal parenchyma (DICE-score) with manual segmentation(At time of CT examination (inclusion and follow up - expected average 12 weeks))
- Comparison of AI-segmentation of renal parenchyma (Hausdorff distance) with manual segmentation(At time of CT examination (inclusion and follow up - expected average 12 weeks))
- Prospective performance (diagnostic accuracy) of AI detection of ureteral obstruction (compared to radiology report (gold standard)(At time of CT examination (inclusion and follow up - expected average 12 weeks))
- Comparison of AI-segmentation of renal pelvis (Dice-score) with manual segmentation(At time of CT examination (inclusion and follow up - expected average 12 weeks))
研究者
Peter Mæhre Lauritzen
Principal investigator
Oslo University Hospital