Artificial intelligence-integrated video analysis of vessel area changes and instrument motion for microsurgical skill assessment
Abstract Mastering microsurgical skills is essential for neurosurgical trainees. Video-based analysis of target tissue changes and surgical instrument motion provides an objective, quantitative method for assessing microsurgical proficiency, potentially enhancing training and patient safety. This st...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-13522-1 |
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| author | Taku Sugiyama Minghui Tang Hiroyuki Sugimori Marin Sakamoto Miki Fujimura |
| author_facet | Taku Sugiyama Minghui Tang Hiroyuki Sugimori Marin Sakamoto Miki Fujimura |
| author_sort | Taku Sugiyama |
| collection | DOAJ |
| description | Abstract Mastering microsurgical skills is essential for neurosurgical trainees. Video-based analysis of target tissue changes and surgical instrument motion provides an objective, quantitative method for assessing microsurgical proficiency, potentially enhancing training and patient safety. This study evaluates the effectiveness of an artificial intelligence (AI)-based video analysis model in assessing microsurgical performance and examines the correlation between AI-derived parameters and specific surgical skill components. A dual AI framework was developed, integrating a semantic segmentation model for artificial blood vessel analysis with an instrument tip-tracking algorithm. These models quantified dynamic vessel area fluctuation, tissue deformation error count, instrument path distance, and normalized jerk index during a single-stitch end-to-side anastomosis task performed by 14 surgeons with varying experience levels. The AI-derived parameters were validated against traditional criteria-based rating scales assessing instrument handling, tissue respect, efficiency, suture handling, suturing technique, operation flow, and overall performance. Rating scale scores correlated with microsurgical experience, exhibiting a bimodal distribution that classified performance into good and poor groups. Video-based parameters showed strong correlations with various skill categories. Receiver operating characteristic analysis demonstrated that combining these parameters improved the discrimination of microsurgical performance. The proposed method effectively captures technical microsurgical skills and can assess performance. |
| format | Article |
| id | doaj-art-28dc0b8ef4484ca594f9264348f67ccf |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-28dc0b8ef4484ca594f9264348f67ccf2025-08-20T03:45:48ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-13522-1Artificial intelligence-integrated video analysis of vessel area changes and instrument motion for microsurgical skill assessmentTaku Sugiyama0Minghui Tang1Hiroyuki Sugimori2Marin Sakamoto3Miki Fujimura4Department of Neurosurgery, Hokkaido University Graduate School of MedicineDepartment of Diagnostic Imaging, Hokkaido University Faculty of Medicine and Graduate School of MedicineMedical AI Research and Development Center, Hokkaido University HospitalGraduate School of Health Sciences, Hokkaido UniversityDepartment of Neurosurgery, Hokkaido University Graduate School of MedicineAbstract Mastering microsurgical skills is essential for neurosurgical trainees. Video-based analysis of target tissue changes and surgical instrument motion provides an objective, quantitative method for assessing microsurgical proficiency, potentially enhancing training and patient safety. This study evaluates the effectiveness of an artificial intelligence (AI)-based video analysis model in assessing microsurgical performance and examines the correlation between AI-derived parameters and specific surgical skill components. A dual AI framework was developed, integrating a semantic segmentation model for artificial blood vessel analysis with an instrument tip-tracking algorithm. These models quantified dynamic vessel area fluctuation, tissue deformation error count, instrument path distance, and normalized jerk index during a single-stitch end-to-side anastomosis task performed by 14 surgeons with varying experience levels. The AI-derived parameters were validated against traditional criteria-based rating scales assessing instrument handling, tissue respect, efficiency, suture handling, suturing technique, operation flow, and overall performance. Rating scale scores correlated with microsurgical experience, exhibiting a bimodal distribution that classified performance into good and poor groups. Video-based parameters showed strong correlations with various skill categories. Receiver operating characteristic analysis demonstrated that combining these parameters improved the discrimination of microsurgical performance. The proposed method effectively captures technical microsurgical skills and can assess performance.https://doi.org/10.1038/s41598-025-13522-1Deep learningEC-IC bypassMicrosurgical trainingObjective surgical skill evaluationSurgical educationTissue deformation |
| spellingShingle | Taku Sugiyama Minghui Tang Hiroyuki Sugimori Marin Sakamoto Miki Fujimura Artificial intelligence-integrated video analysis of vessel area changes and instrument motion for microsurgical skill assessment Scientific Reports Deep learning EC-IC bypass Microsurgical training Objective surgical skill evaluation Surgical education Tissue deformation |
| title | Artificial intelligence-integrated video analysis of vessel area changes and instrument motion for microsurgical skill assessment |
| title_full | Artificial intelligence-integrated video analysis of vessel area changes and instrument motion for microsurgical skill assessment |
| title_fullStr | Artificial intelligence-integrated video analysis of vessel area changes and instrument motion for microsurgical skill assessment |
| title_full_unstemmed | Artificial intelligence-integrated video analysis of vessel area changes and instrument motion for microsurgical skill assessment |
| title_short | Artificial intelligence-integrated video analysis of vessel area changes and instrument motion for microsurgical skill assessment |
| title_sort | artificial intelligence integrated video analysis of vessel area changes and instrument motion for microsurgical skill assessment |
| topic | Deep learning EC-IC bypass Microsurgical training Objective surgical skill evaluation Surgical education Tissue deformation |
| url | https://doi.org/10.1038/s41598-025-13522-1 |
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