This study presents an innovative AI-driven tool for diagnosing Peyronie's Disease (PD), a condition that affects between 0.3% and 13.1% of men worldwide. Our method uses key point detection on both images and videos to measure penile curvature angles, utilizing advanced computer vision techniques. This tool has demonstrated high accuracy in identifying anatomical landmarks, validated against conventional goniometer measurements. Traditional PD diagnosis often involves subjective and invasive methods, which can lead to patient discomfort and inaccuracies. Our approach offers a precise, reliable, and non-invasive diagnostic tool to address these drawbacks. The model distinguishes between PD and normal anatomical changes with a sensitivity of 96.7% and a specificity of 100%. This advancement represents a significant improvement in urological diagnostics, greatly enhancing the efficacy and convenience of PD assessment for healthcare providers and patients.

本研究解决了传统佩罗尼氏病（PD）诊断中的主观性和侵入性问题，提出了一种创新的AI驱动工具，利用计算机视觉技术进行阴茎弯曲角度的测量。该工具通过关键点检测在图像和视频中展示了96.7%的敏感性和100%的特异性，显著提高了泌尿系统诊断的精确性与便捷性，为医疗提供者和患者带来了巨大的益处。

人工智能增强的佩罗尼氏病诊断：一种基于计算机视觉的新方法