AI enhanced diagnosis of Peyronies disease a novel approach using Computer Vision
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917834856071168 |
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| author | Kularathne, Yudara Prathapa, Janitha Sothyrajah, Prarththanan Arasaratnam, Salomi Ambepitiya, Sithira Ahamed, Thanveer Wijesundara, Dinuka |
| author_facet | Kularathne, Yudara Prathapa, Janitha Sothyrajah, Prarththanan Arasaratnam, Salomi Ambepitiya, Sithira Ahamed, Thanveer Wijesundara, Dinuka |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07684 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI enhanced diagnosis of Peyronies disease a novel approach using Computer Vision Kularathne, Yudara Prathapa, Janitha Sothyrajah, Prarththanan Arasaratnam, Salomi Ambepitiya, Sithira Ahamed, Thanveer Wijesundara, Dinuka Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | AI enhanced diagnosis of Peyronies disease a novel approach using Computer Vision |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.07684 |