Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.17391 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915209074966528 |
|---|---|
| author | Deo, Atharva Matsumoto, Nicholas Kim, Sun Wager, Peter Tsai, Randy G. Denmark, Aaron Yang, Cherine Li, Xi Moran, Jay Hernandez, Miguel Hung, Andrew J. |
| author_facet | Deo, Atharva Matsumoto, Nicholas Kim, Sun Wager, Peter Tsai, Randy G. Denmark, Aaron Yang, Cherine Li, Xi Moran, Jay Hernandez, Miguel Hung, Andrew J. |
| contents | We present an AI based approach to automate the End-to-end Assessment of Suturing Expertise (EASE), a suturing skills assessment tool that comprehensively defines criteria around relevant sub-skills.1 While EASE provides granular skills assessment related to suturing to provide trainees with an objective evaluation of their aptitude along with actionable insights, the scoring process is currently performed by human evaluators, which is time and resource consuming. The AI based approach solves this by enabling real-time score prediction with minimal resources during model inference. This enables the possibility of real-time feedback to the surgeons/trainees, potentially accelerating the learning process for the suturing task and mitigating critical errors during the surgery, improving patient outcomes. In this study, we focus on the following 7 EASE domains that come under 3 suturing phases: 1) Needle Handling: Number of Repositions, Needle Hold Depth, Needle Hold Ratio, and Needle Hold Angle; 2) Needle Driving: Driving Smoothness, and Wrist Rotation; 3) Needle Withdrawal: Wrist Rotation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17391 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-driven Automation of End-to-end Assessment of Suturing Expertise Deo, Atharva Matsumoto, Nicholas Kim, Sun Wager, Peter Tsai, Randy G. Denmark, Aaron Yang, Cherine Li, Xi Moran, Jay Hernandez, Miguel Hung, Andrew J. Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning We present an AI based approach to automate the End-to-end Assessment of Suturing Expertise (EASE), a suturing skills assessment tool that comprehensively defines criteria around relevant sub-skills.1 While EASE provides granular skills assessment related to suturing to provide trainees with an objective evaluation of their aptitude along with actionable insights, the scoring process is currently performed by human evaluators, which is time and resource consuming. The AI based approach solves this by enabling real-time score prediction with minimal resources during model inference. This enables the possibility of real-time feedback to the surgeons/trainees, potentially accelerating the learning process for the suturing task and mitigating critical errors during the surgery, improving patient outcomes. In this study, we focus on the following 7 EASE domains that come under 3 suturing phases: 1) Needle Handling: Number of Repositions, Needle Hold Depth, Needle Hold Ratio, and Needle Hold Angle; 2) Needle Driving: Driving Smoothness, and Wrist Rotation; 3) Needle Withdrawal: Wrist Rotation. |
| title | AI-driven Automation of End-to-end Assessment of Suturing Expertise |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.17391 |