实施深度学习以测量阴茎曲的真实2D手术内图像
Sriman Bidhan Baray1, Saidanvar Agzamkhodjaev2, Mansour Ali3
1Department of Electrical and Electronic Engineering, University of Dhaka, Bangladesh.
Journal of pediatric urology
|January 29, 2026
概括
一个新的AI系统使用深度学习准确地测量阴茎曲率 (PC),为低血压手术提供一致和可靠的评估. 这种自动化工具有助于外科医生精确评估患者.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
背景情况:
- 阴茎曲 (PC) 影响高达10%的男性出生,通常与hypospadias相关.
- 当前的PC评估方法缺乏一致性,影响手术结果.
- 需要一种可靠的,自动化的PC评估技术.
研究的目的:
- 开发可靠的深度学习解决方案,用于精确的PC评估.
- 使用实时手术内二维图像自动化PC评估.
- 提高PC测量的一致性和可靠性.
主要方法:
- 在421张专家注释的图像上训练有素的深度学习模型 (YOLOv8,HRNet).
- 利用图像细分来生成阴茎轴面具.
- 开发了一种基于关键点的方法来自动计算PC角度.
主要成果:
- 人工智能系统在阴茎区域定位方面实现了99.4%的精度.
- 细分表现显示了高的交叉在欧盟 (83.56%) 和子相似性 (91.02%).
- 角度预测的平均绝对误差为7.9°,相当于或比人类评分器 (6.5-12.0°) 更好.
结论:
- 深度学习和关键点测量为PC评估提供了可靠的方法.
- 人工智能工具可以显著提高外科医生对患者的评估.
- 这项技术对低血压病学研究和临床决策具有前景.
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