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Cited by 40Surgical Simulation and TrainingInnovations in Medical EducationArtificial Intelligence in Healthcare and Education

Developing artificial intelligence models for medical student suturing and knot-tying video-based assessment and coaching

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Nagaraj, Madhuri B., Namazi, Babak, Sankaranarayanan, Ganesh, Scott, Daniel J.·2023·Surgical Endoscopy
DOI 10.1007/s00464-022-09509-yPMC PMC9388210
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Abstract

Background Early introduction and distributed learning have been shown to improve student comfort with basic requisite suturing skills. The need for more frequent and directed feedback, however, remains an enduring concern for both remote and in-person training. A previous in-person curriculum for our second-year medical students transitioning to clerkships was adapted to an at-home video-based assessment model due to the social distancing implications of COVID-19. We aimed to develop an Artificial Intelligence (AI) model to perform video-based assessment.Methods Second-year medical students were asked to submit a video of a simple interrupted knot on a penrose drain with instrument tying technique after self-training to proficiency. Proficiency was defined as performing the task under two minutes with no critical errors. All the videos were first manually rated with a pass-fail rating and then subsequently underwent task segmentation. We developed and trained two AI models based on convolutional neural networks to identify errors (instrument holding and knot-tying) and provide automated ratings.Results A total of 229 medical student videos were reviewed (150 pass, 79 fail). Of those who failed, the critical error distribution was 15 knot-tying, 47 instrument-holding, and 17 multiple. A total of 216 videos were used to train the models after excluding the low-quality videos. A k-fold cross-validation (k = 10) was used. The accuracy of the instrument holding model was 89% with an F-1 score of 74%. For the knot-tying model, the accuracy was 91% with an F-1 score of 54%.Conclusions Medical students require assessment and directed feedback to better acquire surgical skill, but this is often time-consuming and inadequately done. AI techniques can instead be employed to perform automated surgical video analysis. Future work will optimize the current model to identify discrete errors in order to supplement video-based rating with specific feedback.

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  • In surgery and surgical subspecialties, AI has been used to learn and practice knot tying and laparoscopy training based on motion analysis and machine learning (ML); AI can enhance VR for medical skills training; one study reported unintended outcomes with both positive and negative consequences for learner competency in surgical skills, suggesting that intervention from human tutors may be

    “In surgery and surgical subspecialties [86], it has been used to learn and practice knot tying [87] and laparoscopy training based on motion analysis and machine learning (ML) [88]. AI can enhance VR for medical skills training [89]. One study reported unintended outcomes with both positive and negative consequences for learner competency in surgical skills, suggesting that intervention from human”

    From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [87]

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  • In one study, two AI models based on convolutional neural networks were trained to identify errors and provide automated ratings for suturing videos, with overall accuracy of 83%.

    “two AI models based on convolutional neural networks were trained to identify errors and provide automated ratings; the overall accuracy was 83%.”

    From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [87]

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Authors
Nagaraj, M. B., Namazi, B., Sankaranarayanan, G., & Scott, D. J.
Affiliations
The University of Texas Southwestern Medical Center (US)
Corresponding author
Nagaraj, Madhuri B.
ORCID
Venue
Surgical Endoscopy
Publisher
Springer Science and Business Media LLC
Published
2022-08-18
Volume
37
Issue
1
Pages
402-411
ISSN
  • 0930-2794 (ISSN-L)
  • 1432-2218
DOI
10.1007/s00464-022-09509-y
PMC
PMC9388210
OpenAlex
W4293799563
Cited by
40
References
32
Citations by year
2026: 10 · 2025: 14 · 2024: 7 · 2023: 9
FWCI
7.17
Type
journal article
Language
en
Open access
Licence
Locations
  • Surgical Endoscopy (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
University of Texas Southwestern Medical Center
Topics
  • Health Sciences › Medicine › Surgery › Surgical Simulation and Training
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
suturing skills, knot tying, video-based assessment, convolutional neural networks, medical students, remote surgical training, COVID-19 social distancing
MeSH terms
Mentoring, COVID-19, Artificial Intelligence, Clinical Competence, Humans, Students, Medical, Suture Techniques, Videotape Recording

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