便携式超声波膀体积测量在整个体积范围使用深度学习的人工智能模型在一个选定的队列:原则的证明研究研究
Hyun Ju Jeong1, Aeran Seol2, Seungjun Lee2,3
1Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Republic of Korea.
Neurourology and urodynamics
|May 19, 2025
概括
深度学习的人工智能膀体积 (AI-BV) 测量比使用便携式超声波膀扫描仪测量传统膀体积 (C-BV) 更准确. 与真正的膀体积 (T-BV) 相比,AI-BV显示出明显较小的差异.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 精确的膀体积测量对于诊断下尿路症状至关重要.
- 使用便携式超声波膀扫描仪 (PUBS) 的传统方法在准确性上可能存在局限性.
研究的目的:
- 为了比较人工智能辅助膀体积 (AI-BV) 测量与使用PUBS进行常规膀体积 (C-BV) 测量的准确性.
- 为了确定AI算法是否可以改善膀体积估计.
主要方法:
- 对250名接受填充囊泡测试的患者进行前性调查.
- 使用PUBS (C-BV) 和深度学习AI模型 (AI-BV) 来测量膀体积.
- 对AI-BV和C-BV与真正的膀体积 (T-BV) 的比较.
主要成果:
- 与T-BV (p=0.081) 相比,AI-BV没有显著差异,与C-BV (p=0.001) 不同.
- AI-BV和T-BV之间的平均差异明显小于C-BV和T-BV之间的差异 (6.5vs15.0毫升).
- 两种AI-BV (R2=0.90) 和C-BV (R2=0.91) 都与T-BV高度相关.
结论:
- 深度学习AI-BV在研究的队列中提供比传统方法更准确的膀体积估计.
- 需要进一步的研究来确定外部人群中的临床相关性和性能.
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