Jeong Hyun Ju, Seol Aeran, Lee Seungjun, Lim Hyunji, Lee Maria, Oh Seung-June
Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Republic of Korea.
Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Neurourol Urodyn. 2025 Aug;44(6):1238-1244. doi: 10.1002/nau.70057. Epub 2025 May 19.
OBJECTIVE: We aimed to prospectively investigate whether bladder volume measured using deep learning artificial intelligence (AI) algorithms (AI-BV) is more accurate than that measured using conventional methods (C-BV) if using a portable ultrasound bladder scanner (PUBS). PATIENTS AND METHODS: Patients who underwent filling cystometry because of lower urinary tract symptoms between January 2021 and July 2022 were enrolled. Every time the bladder was filled serially with normal saline from 0 mL to maximum cystometric capacity in 50 mL increments, C-BV was measured using PUBS. Ultrasound images obtained during this process were manually annotated to define the bladder contour, which was used to build a deep learning AI model. The true bladder volume (T-BV) for each bladder volume range was compared with C-BV and AI-BV for analysis. RESULTS: We enrolled 250 patients (213 men and 37 women), and a deep learning AI model was established using 1912 bladder images. There was a significant difference between C-BV (205.5 ± 170.8 mL) and T-BV (190.5 ± 165.7 mL) (p = 0.001), but no significant difference between AI-BV (197.0 ± 161.1 mL) and T-BV (190.5 ± 165.7 mL) (p = 0.081). In bladder volume ranges of 101-150, 151-200, and 201-300 mL, there were significant differences in the percentage of volume differences between [C-BV and T-BV] and [AI-BV and T-BV] (p < 0.05), but no significant difference if converted to absolute values (p > 0.05). C-BV (R = 0.91, p < 0.001) and AI-BV (R = 0.90, p < 0.001) were highly correlated with T-BV. The mean difference between AI-BV and T-BV (6.5 ± 50.4) was significantly smaller than that between C-BV and T-BV (15.0 ± 50.9) (p = 0.001). CONCLUSION: Following image pre-processing, deep learning AI-BV more accurately estimated true BV than conventional methods in this selected cohort on internal validation. Determination of the clinical relevance of these findings and performance in external cohorts requires further study. TRIAL REGISTRATION: The clinical trial was conducted using an approved product for its approved indication, so approval from the Ministry of Food and Drug Safety (MFDS) was not required. Therefore, there is no clinical trial registration number.
目的：我们旨在前瞻性研究，在使用便携式超声膀胱扫描仪（PUBS）时，采用深度学习人工智能（AI）算法测量的膀胱容量（AI - BV）是否比传统方法测量的膀胱容量（C - BV）更准确。 患者与方法：纳入2021年1月至2022年7月因下尿路症状接受膀胱容量测定的患者。每次膀胱以50 mL增量从0 mL连续灌注生理盐水至最大膀胱容量测定值，在此过程中使用PUBS测量C - BV。对该过程中获得的超声图像进行手动标注以确定膀胱轮廓，用于构建深度学习AI模型。比较每个膀胱容量范围的真实膀胱容量（T - BV）与C - BV和AI - BV进行分析。 结果：我们纳入了250例患者（213例男性和37例女性），并使用1912张膀胱图像建立了深度学习AI模型。C - BV（205.5±170.8 mL）与T - BV（190.5±165.7 mL）之间存在显著差异（p = 0.001），但AI - BV（197.0±161.1 mL）与T - BV（190.5±165.7 mL）之间无显著差异（p = 0.081）。在膀胱容量范围为101 - 150、151 - 200和201 - 300 mL时，[C - BV与T - BV]和[AI - BV与T - BV]之间的容量差异百分比存在显著差异（p < 0.05），但转换为绝对值后无显著差异（p > 0.05）。C - BV（R = 0.91，p < 0.001）和AI - BV（R = 0.90，p < 0.001）与T - BV高度相关。AI - BV与T - BV之间的平均差异（6.5±50.4）显著小于C - BV与T - BV之间的平均差异（15.0±50.9）（p = 0.001）。 结论：在内部验证中，经过图像预处理后，在该选定队列中深度学习AI - BV比传统方法更准确地估计了真实膀胱容量。确定这些发现的临床相关性及其在外部队列中的表现需要进一步研究。 试验注册：本临床试验使用的是经批准用于其批准适应症的产品，因此无需食品药品安全部（MFDS）批准。所以，没有临床试验注册号。
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