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.

翻译：本研究提出了一种创新的AI驱动工具，用于诊断佩罗尼氏病（PD），这是一种影响全球0.3%至13.1%男性的疾病。我们的方法利用先进的计算机视觉技术，通过对图像和视频进行关键点检测来测量阴茎弯曲角度。该工具在识别解剖标志方面表现出高精度，并已通过传统测角仪测量验证。传统的PD诊断通常涉及主观且有创的方法，可能导致患者不适和结果不准确。我们的方法提供了一种精确、可靠且无创的诊断工具以解决这些缺陷。该模型区分PD与正常解剖变化的灵敏度为96.7%，特异性为100%。这一进展代表了泌尿科诊断领域的显著进步，极大地提升了医疗保健提供者和患者进行PD评估的效能与便利性。