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Paper Abstract and Keywords
Presentation 2026-03-25 15:00
Surgical Suturing Skill Estimation Based on Suture Geometry and Video Data
Reo Tanaka, Yota Koshimoto, Hiroki Kajita, Mariko Isogawa (Keio Univ.) PRMU2025-63
Abstract (in Japanese) (See Japanese page) 
(in English) With the widespread adoption of minimally invasive surgery, demand for objective proficiency evaluation systems has surged. Conventional visual assessment suffers from high costs and subjectivity, while existing video analysis methods primarily focus on process evaluation, such as smoothness, often overlooking the quality of the final suture. This makes it difficult to identify intermediate surgeons, whose movements resemble experts' but whose result precision varies. To address this, we propose a method fusing a segmentation model with classical algorithms like edge detection. This approach automatically quantifies geometric suture metrics—including interval, angle, count, and entry-exit distances—and integrates them with video features to determine proficiency. Experiments on the AIxSuture dataset demonstrated an F1 score improvement of 0.0917 over the baseline. Notably, the F1 score for intermediate surgeons improved by 0.2280, confirming the method's effectiveness.
Keyword (in Japanese) (See Japanese page) 
(in English) Surgical skill assessment / Deep Learning / Segmentation / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 424, PRMU2025-63, pp. 120-125, March 2026.
Paper # PRMU2025-63 
Date of Issue 2026-03-17 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF PRMU2025-63

Conference Information
Committee PRMU IPSJ-CVIM IBISML ITE-SIP  
Conference Date 2026-03-24 - 2026-03-25 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2026-03-PRMU-CVIM-IBISML-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Surgical Suturing Skill Estimation Based on Suture Geometry and Video Data 
Sub Title (in English)  
Keyword(1) Surgical skill assessment  
Keyword(2) Deep Learning  
Keyword(3) Segmentation  
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1st Author's Name Reo Tanaka  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Yota Koshimoto  
2nd Author's Affiliation Keio University (Keio Univ.)
3rd Author's Name Hiroki Kajita  
3rd Author's Affiliation Keio University (Keio Univ.)
4th Author's Name Mariko Isogawa  
4th Author's Affiliation Keio University (Keio Univ.)
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Speaker Author-1 
Date Time 2026-03-25 15:00:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2025-63 
Volume (vol) vol.125 
Number (no) no.424 
Page pp.120-125 
#Pages 6 
Date of Issue 2026-03-17 (PRMU) 

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