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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.18836 |
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| _version_ | 1866909752983814144 |
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| author | Hu, Luyin Gholami, Soheil Dindelegan, George Meling, Torstein R. Billard, Aude |
| author_facet | Hu, Luyin Gholami, Soheil Dindelegan, George Meling, Torstein R. Billard, Aude |
| contents | Microsurgical anastomosis demands exceptional dexterity and visuospatial skills, underscoring the importance of comprehensive training and precise outcome assessment. Currently, methods such as the outcome-oriented anastomosis lapse index are used to evaluate this procedure. However, they often rely on subjective judgment, which can introduce biases that affect the reliability and efficiency of the assessment of competence. Leveraging three datasets from hospitals with participants at various levels, we introduce a quantitative framework that uses image-processing techniques for objective assessment of microsurgical anastomoses. The approach uses geometric modeling of errors along with a detection and scoring mechanism, enhancing the efficiency and reliability of microsurgical proficiency assessment and advancing training protocols. The results show that the geometric metrics effectively replicate expert raters' scoring for the errors considered in this work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18836 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantitative Outcome-Oriented Assessment of Microsurgical Anastomosis Hu, Luyin Gholami, Soheil Dindelegan, George Meling, Torstein R. Billard, Aude Computer Vision and Pattern Recognition Microsurgical anastomosis demands exceptional dexterity and visuospatial skills, underscoring the importance of comprehensive training and precise outcome assessment. Currently, methods such as the outcome-oriented anastomosis lapse index are used to evaluate this procedure. However, they often rely on subjective judgment, which can introduce biases that affect the reliability and efficiency of the assessment of competence. Leveraging three datasets from hospitals with participants at various levels, we introduce a quantitative framework that uses image-processing techniques for objective assessment of microsurgical anastomoses. The approach uses geometric modeling of errors along with a detection and scoring mechanism, enhancing the efficiency and reliability of microsurgical proficiency assessment and advancing training protocols. The results show that the geometric metrics effectively replicate expert raters' scoring for the errors considered in this work. |
| title | Quantitative Outcome-Oriented Assessment of Microsurgical Anastomosis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.18836 |