OSS: Open Suturing Skills Vision-Based Assessment Challenge 2024-2025
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arXiv
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| Natura: | Preprint |
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2026
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| author | Hoffmann, Hanna Bady, Setareh de Boer, Claas Kirchner, Max Egger, Jan Röhrig, Rainer Hölzle, Frank Gruber, Lennart Johannes Xie, Kunpeng Neuhaus, Marlon Alves, Victor Barbosa, Guilherme Barroso, Leonardo Carvalho, João Chen, Hao d'Albenzio, Gabriella Ferreira, André Gomes, Nuno Hayashi, Yuichiro Hirasawa, Kousuke Hisey, Rebecca Hong, Seungjae Jeong, Seoi Jesus, Tiago Kang, Daehong Kasai, Satoshi Kikuchi, Shunsuke Kitasaka, Takayuki Kondo, Satoshi Kong, Hyoun-Joong Kong, Youngbin Kouno, Atsushi Laufer, Shlomi Lee, Kyu Eun Long, Bining Maghsoodi, Nooshin Matsuzaki, Hiroki Mazomenos, Evangelos Meiraz, Ori Mori, Kensaku Music, Marina Oda, Masahiro Papo, Roi Park, Jieun Piexoto, Rafael Rezaei, Saeid Ribeiro, Mariana Shin, Soyeon Shu, Yang Smoller, Idan Stoyanov, Danail Wang, Yihui Zhao, Xinkai Bodenstedt, Sebastian Funke, Isabel Speidel, Stefanie Hinrichs-Puladi, Behrus |
| author_facet | Hoffmann, Hanna Bady, Setareh de Boer, Claas Kirchner, Max Egger, Jan Röhrig, Rainer Hölzle, Frank Gruber, Lennart Johannes Xie, Kunpeng Neuhaus, Marlon Alves, Victor Barbosa, Guilherme Barroso, Leonardo Carvalho, João Chen, Hao d'Albenzio, Gabriella Ferreira, André Gomes, Nuno Hayashi, Yuichiro Hirasawa, Kousuke Hisey, Rebecca Hong, Seungjae Jeong, Seoi Jesus, Tiago Kang, Daehong Kasai, Satoshi Kikuchi, Shunsuke Kitasaka, Takayuki Kondo, Satoshi Kong, Hyoun-Joong Kong, Youngbin Kouno, Atsushi Laufer, Shlomi Lee, Kyu Eun Long, Bining Maghsoodi, Nooshin Matsuzaki, Hiroki Mazomenos, Evangelos Meiraz, Ori Mori, Kensaku Music, Marina Oda, Masahiro Papo, Roi Park, Jieun Piexoto, Rafael Rezaei, Saeid Ribeiro, Mariana Shin, Soyeon Shu, Yang Smoller, Idan Stoyanov, Danail Wang, Yihui Zhao, Xinkai Bodenstedt, Sebastian Funke, Isabel Speidel, Stefanie Hinrichs-Puladi, Behrus |
| contents | Achieving high levels of surgical skill through effective training is essential for optimal patient outcomes. Automated, data-driven skill assessment holds significant potential to improve surgical training. While machine learning-based methods are increasingly popular for assessing skills in minimally invasive surgery, their application to open surgery remains limited. We present the results of a dedicated MICCAI challenge designed to benchmark and advance vision-based skill assessment in open surgery.
The challenge dataset comprises videos of an open suturing training task recorded with a static GoPro camera in a dry-lab setting, with instrument trajectories available in addition to the primary video modality. The OSS Challenge was hosted over two consecutive years, comprising two and three independent tasks, respectively: (1) classifying skill level into four classes, (2) predicting the full Objective Structured Assessment of Technical Skills across eight categories, and (3) tracking hands and surgical tools. Participants submitted diverse solutions including deep learning-based video models, tracking-driven methods, and hybrid approaches.
General-purpose spatiotemporal video models consistently achieved the strongest performance, though conceptually diverse approaches reached competitive levels when well-executed. Predicting fine-grained OSATS scores remains challenging but benefits substantially from increased training data. Keypoint tracking proves difficult given frequent occlusions and out-of-frame instances, limiting current applicability for motion-based skill analysis. This work benchmarks innovative and diverse solutions for surgical skill assessment, highlighting both the promise and current limitations of video-based evaluation in open surgery and identifying critical directions for advancing automated skill assessment toward clinical impact. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22200 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OSS: Open Suturing Skills Vision-Based Assessment Challenge 2024-2025 Hoffmann, Hanna Bady, Setareh de Boer, Claas Kirchner, Max Egger, Jan Röhrig, Rainer Hölzle, Frank Gruber, Lennart Johannes Xie, Kunpeng Neuhaus, Marlon Alves, Victor Barbosa, Guilherme Barroso, Leonardo Carvalho, João Chen, Hao d'Albenzio, Gabriella Ferreira, André Gomes, Nuno Hayashi, Yuichiro Hirasawa, Kousuke Hisey, Rebecca Hong, Seungjae Jeong, Seoi Jesus, Tiago Kang, Daehong Kasai, Satoshi Kikuchi, Shunsuke Kitasaka, Takayuki Kondo, Satoshi Kong, Hyoun-Joong Kong, Youngbin Kouno, Atsushi Laufer, Shlomi Lee, Kyu Eun Long, Bining Maghsoodi, Nooshin Matsuzaki, Hiroki Mazomenos, Evangelos Meiraz, Ori Mori, Kensaku Music, Marina Oda, Masahiro Papo, Roi Park, Jieun Piexoto, Rafael Rezaei, Saeid Ribeiro, Mariana Shin, Soyeon Shu, Yang Smoller, Idan Stoyanov, Danail Wang, Yihui Zhao, Xinkai Bodenstedt, Sebastian Funke, Isabel Speidel, Stefanie Hinrichs-Puladi, Behrus Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Achieving high levels of surgical skill through effective training is essential for optimal patient outcomes. Automated, data-driven skill assessment holds significant potential to improve surgical training. While machine learning-based methods are increasingly popular for assessing skills in minimally invasive surgery, their application to open surgery remains limited. We present the results of a dedicated MICCAI challenge designed to benchmark and advance vision-based skill assessment in open surgery. The challenge dataset comprises videos of an open suturing training task recorded with a static GoPro camera in a dry-lab setting, with instrument trajectories available in addition to the primary video modality. The OSS Challenge was hosted over two consecutive years, comprising two and three independent tasks, respectively: (1) classifying skill level into four classes, (2) predicting the full Objective Structured Assessment of Technical Skills across eight categories, and (3) tracking hands and surgical tools. Participants submitted diverse solutions including deep learning-based video models, tracking-driven methods, and hybrid approaches. General-purpose spatiotemporal video models consistently achieved the strongest performance, though conceptually diverse approaches reached competitive levels when well-executed. Predicting fine-grained OSATS scores remains challenging but benefits substantially from increased training data. Keypoint tracking proves difficult given frequent occlusions and out-of-frame instances, limiting current applicability for motion-based skill analysis. This work benchmarks innovative and diverse solutions for surgical skill assessment, highlighting both the promise and current limitations of video-based evaluation in open surgery and identifying critical directions for advancing automated skill assessment toward clinical impact. |
| title | OSS: Open Suturing Skills Vision-Based Assessment Challenge 2024-2025 |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.22200 |