Alpert Evan Avraham, Gold Daniel David, Kobliner-Friedman Deganit, Wagner Michael, Dadon Ziv
Department of Emergency Medicine, Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem 9112001, Israel.
Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9190500, Israel.
Diagnostics (Basel). 2024 Aug 22;14(16):1829. doi: 10.3390/diagnostics14161829.
Measuring elevated post-void residual volume is important for diagnosing urinary outflow tract obstruction and cauda equina syndrome. Catheter placement is exact but painful, invasive, and may cause infection, whereas an ultrasound is accurate, painless, and safe.
The purpose of this single-center study is to evaluate the accuracy of a module for artificial-intelligence (AI)-based fully automated bladder volume (BV) prospective measurement using two-dimensional ultrasound images, as compared with manual measurement by expert sonographers.
Pairs of transverse and longitudinal bladder images were obtained from patients evaluated in an urgent care clinic. The scans were prospectively analyzed by the automated module using the prolate ellipsoid method. The same examinations were manually measured by a blinded expert sonographer. The two methods were compared using the Pearson correlation, kappa coefficients, and the Bland-Altman method.
A total of 111 pairs of transverse and longitudinal views were included. A very strong correlation was found between the manual BV measurements and the AI-based module with r = 0.97 [95% CI: 0.96-0.98]. The specificity and sensitivity for the diagnosis of an elevated post-void residual volume using a threshold ≥200 mL were 1.00 and 0.82, respectively. An almost-perfect agreement between manual and automated methods was obtained (kappa = 0.85). Perfect reproducibility was found for both inter- and intra-observer agreements.
This AI-based module provides an accurate automated measurement of the BV based on ultrasound images. This novel method demonstrates a very strong correlation with the gold standard, making it a potentially valuable decision-support tool for non-experts in acute settings.
测量排尿后残余尿量升高对于诊断尿路梗阻和马尾综合征很重要。插入导尿管操作精确，但会引起疼痛、具有侵入性，且可能导致感染，而超声检查准确、无痛且安全。
本单中心研究的目的是评估基于人工智能（AI）的模块使用二维超声图像进行全自动膀胱容量（BV）前瞻性测量的准确性，并与专业超声检查人员的手动测量结果进行比较。
从在紧急护理诊所接受评估的患者中获取成对的膀胱横切面和纵切面图像。使用长椭球体方法通过自动模块对扫描图像进行前瞻性分析。由一位不知情的专业超声检查人员对相同的检查进行手动测量。使用Pearson相关性、kappa系数和Bland-Altman方法对两种方法进行比较。
共纳入111对横切面和纵切面视图。手动测量的BV与基于AI的模块之间发现了非常强的相关性，r = 0.97 [95% CI：0.96 - 0.98]。使用阈值≥200 mL诊断排尿后残余尿量升高的特异性和敏感性分别为1.00和0.82。手动和自动方法之间获得了几乎完美的一致性（kappa = 0.85）。观察者间和观察者内一致性均具有完美的可重复性。
这个基于AI的模块可基于超声图像对BV进行准确的自动测量。这种新方法与金标准显示出非常强的相关性，使其成为急性情况下非专业人员潜在有价值的决策支持工具。
