Optimizing anterior urethral stricture assessment: leveraging AI-assisted three-dimensional sonourethrography in clinical practice.

IF 1.8 4区 医学 Q3 UROLOGY & NEPHROLOGY International Urology and Nephrology Pub Date : 2024-12-01 Epub Date: 2024-07-02 DOI:10.1007/s11255-024-04137-y
Chao Feng, Qi-Jie Lu, Jing-Dong Xue, Hui-Quan Shu, Ying-Long Sa, Yue-Min Xu, Lei Chen
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Abstract

Purpose: This investigation sought to validate the clinical precision and practical applicability of AI-enhanced three-dimensional sonographic imaging for the identification of anterior urethral stricture.

Methods: The study enrolled 63 male patients with diagnosed anterior urethral strictures alongside 10 healthy volunteers to serve as controls. The imaging protocol utilized a high-frequency 3D ultrasound system combined with a linear stepper motor, which enabled precise and rapid image acquisition. For image analysis, an advanced AI-based segmentation process using a modified U-net algorithm was implemented to perform real-time, high-resolution segmentation and three-dimensional reconstruction of the urethra. A comparative analysis was performed against the surgically measured stricture lengths. Spearman's correlation analysis was executed to assess the findings.

Results: The AI model completed the entire processing sequence, encompassing recognition, segmentation, and reconstruction, within approximately 5 min. The mean intraoperative length of urethral stricture was determined to be 14.4 ± 8.4 mm. Notably, the mean lengths of the urethral strictures reconstructed by manual and AI models were 13.1 ± 7.5 mm and 13.4 ± 7.2 mm, respectively. Interestingly, no statistically significant disparity in urethral stricture length between manually reconstructed and AI-reconstructed images was observed. Spearman's correlation analysis underscored a more robust association of AI-reconstructed images with intraoperative urethral stricture length than manually reconstructed 3D images (0.870 vs. 0.820). Furthermore, AI-reconstructed images provided detailed views of the corpus spongiosum fibrosis from multiple perspectives.

Conclusions: The research heralds the inception of an innovative, efficient AI-driven sonographic approach for three-dimensional visualization of urethral strictures, substantiating its viability and superiority in clinical application.

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优化前尿道狭窄评估:在临床实践中利用人工智能辅助三维声尿道造影。
目的:本研究旨在验证人工智能增强三维声学成像在识别前尿道狭窄方面的临床精确性和实用性:该研究招募了 63 名确诊为前尿道狭窄的男性患者和 10 名健康志愿者作为对照。成像方案采用了高频三维超声系统,该系统与线性步进电机相结合,可实现精确、快速的图像采集。在图像分析方面,采用了先进的人工智能分割程序,使用改进的 U-net 算法对尿道进行实时、高分辨率的分割和三维重建。与手术测量的狭窄长度进行了对比分析。对结果进行了斯皮尔曼相关性分析:结果:人工智能模型在大约 5 分钟内完成了整个处理过程,包括识别、分割和重建。术中确定尿道狭窄的平均长度为 14.4 ± 8.4 毫米。值得注意的是,人工和人工智能模型重建的尿道狭窄平均长度分别为 13.1 ± 7.5 毫米和 13.4 ± 7.2 毫米。有趣的是,人工重建和人工智能重建的尿道狭窄长度在统计学上没有明显差异。斯皮尔曼相关性分析表明,人工智能重建图像与术中尿道狭窄长度的相关性比手动重建的三维图像更强(0.870 对 0.820)。此外,人工智能重建图像从多个角度提供了海绵体纤维化的详细视图:这项研究预示着一种创新、高效的人工智能驱动声像图方法的诞生,该方法用于尿道狭窄的三维可视化,证实了其在临床应用中的可行性和优越性。
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来源期刊
International Urology and Nephrology
International Urology and Nephrology 医学-泌尿学与肾脏学
CiteScore
3.40
自引率
5.00%
发文量
329
审稿时长
1.7 months
期刊介绍: International Urology and Nephrology publishes original papers on a broad range of topics in urology, nephrology and andrology. The journal integrates papers originating from clinical practice.
期刊最新文献
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