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Choksi et al. Art Int Surg. 2025;5:160-9 Artificial
DOI: 10.20517/ais.2024.84
Intelligence Surgery

Original Article Open Access

Automatic assessment of robotic suturing utilizing
computer vision in a dry-lab simulation

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Sarah Choksi 1,2 , Sanjeev Narasimhan , Mattia Ballo , Mehmet Turkcan , Yiran Hu , Chengbo Zang , Alex
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Farrell , Brianna King , Jeffrey Nussbaum , Adin Reisner , Zoran Kostic , Giovanni Taffurelli , Filippo
Filicori 2,6
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Department of General Surgery, Albany Medical Center, Albany, NY 12208, USA.
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Department of Surgery, Northwell Health, New Hyde Park, NY 11042, USA.
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Department of Computer Science, Columbia University, New York, NY 10027, USA.
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Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.
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Department of Surgery, Ospedale S. Maria delle Croci, AUSL Romagna, Ravenna 48121, Italy.
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Zucker School of Medicine at Hofstra/Northwell Health, Hempstead, NY 11549, USA.
Correspondence to: Dr. Sarah Choksi, Department of General Surgery, Albany Medical Center, 43 New Scotland, Albany, NY
12208, USA. E-mail: Sarah.choksi@outlook.com
How to cite this article: Choksi S, Narasimhan S, Ballo M, Turkcan M, Hu Y, Zang C, Farrell A, King B, Nussbaum J, Reisner A,
Kostic Z, Taffurelli G, Filicori F. Automatic assessment of robotic suturing utilizing computer vision in a dry-lab simulation. Art Int
Surg. 2025;5:160-9. https://dx.doi.org/10.20517/ais.2024.84
Received: 29 Sep 2024 First Decision: 9 Jan 2025 Revised: 19 Feb 2025 Accepted: 3 Mar 2025 Published: 1 Apr 2025
Academic Editors: Eyad Elyan, Thomas Schnelldorfer Copy Editor: Ting-Ting Hu Production Editor: Ting-Ting Hu
Abstract
Aim: Automated surgical skill assessment is poised to become an invaluable asset in surgical residency training. In
our study, we aimed to create deep learning (DL) computer vision artificial intelligence (AI) models capable of
automatically assessing trainee performance and determining proficiency on robotic suturing tasks.
Methods: Participants performed two robotic suturing tasks on a bench-top model created by our lab. Videos were
recorded of each surgeon performing a backhand suturing task and a railroad suturing task at 30 frames per second
(FPS) and downsampled to 15 FPS for the study. Each video was segmented into four sub-stitch phases: needle
positioning, targeting, driving, and withdrawal. Each sub-stitch was annotated with a binary technical score (ideal
or non-ideal), reflecting the operator’s skill while performing the suturing action. For DL analysis, 16-frame
overlapping clips were sampled from the videos with a stride of 1. To extract the features useful for classification,
two pretrained Video Swin Transformer models were fine-tuned using these clips: one to classify the sub-stitch
phase and another to predict the technical score. The model outputs were then combined and used to train a
Random Forest Classifier to predict the surgeon's proficiency level.

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
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