ArticleActa neurochirurgica2024
Deep learning-based video-analysis of instrument motion in microvascular anastomosis training.
Article in Acta neurochirurgica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Article
- VasSurDetect: An AI-based Chatbot for Rapid, Photo-based Feedback in Microsurgical Suturing Training.Plastic and reconstructive surgery. Global open · 2026Article
- Vision-Capable LLMs in Microsurgery: A Blinded Comparison of Two AI Models with Expert Microsurgeons in the Appraisal of 200 Experimental Anastomoses.Medical sciences (Basel, Switzerland) · 2026Article
- Artificial intelligence-enhanced microsurgical training: a systematic review.NPJ digital medicine · 2026Article
- Artificial intelligence in microsurgery and supermicrosurgery training within plastic surgery: A systematic review.JPRAS open · 2025Review
- Analyzing Procedural Time and Its Relationship to Performance in Microsurgical Training: A Pilot Study.Cureus · 2025Article
- Artificial Intelligence and Machine Learning in Reconstructive Microsurgery.Seminars in plastic surgery · 2025Review
- Artificial intelligence-integrated video analysis of vessel area changes and instrument motion for microsurgical skill assessment.Scientific reports · 2025Article
- Modeling the Microsurgical Learning Curve Using a Poisson-Based Statistical Approach for Skill Assessment.Cureus · 2025Article
- Artificial Intelligence for Patient Safety and Surgical Education in Neurosurgery.JMA journal · 2025Review
- Artificial intelligence integration in surgery through hand and instrument tracking: a systematic literature review.Frontiers in surgery · 2025Review
- Decoding the Impact of AI on Microsurgery: Systematic Review and Classification of Six Subdomains for Future Development.Plastic and reconstructive surgery. Global open · 2024Article
- Assessment of changes in vessel area during needle manipulation in microvascular anastomosis using a deep learning-based semantic segmentation algorithm: A pilot study.Neurosurgical review · 2024Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
purposeAttaining sufficient microsurgical skills is paramount for neurosurgical trainees. Kinematic analysis of surgical instruments using video offers the potential for an objective assessment of microsurgical proficiency, thereby enhancing surgical training and patient safety. The purposes of this study were to develop a deep-learning-based automated instrument tip-detection algorithm, and to validate its performance in microvascular anastomosis training.
methodsAn automated instrument tip-tracking algorithm was developed and trained using YOLOv2, based on clinical microsurgical videos and microvascular anastomosis practice videos. With this model, we measured motion economy (procedural time and path distance) and motion smoothness (normalized jerk index) during the task of suturing artificial blood vessels for end-to-side anastomosis. These parameters were validated using traditional criteria-based rating scales and were compared across surgeons with varying microsurgical experience (novice, intermediate, and expert). The suturing task was deconstructed into four distinct phases, and parameters within each phase were compared between novice and expert surgeons.
resultsThe high accuracy of the developed model was indicated by a mean Dice similarity coefficient of 0.87. Deep learning-based parameters (procedural time, path distance, and normalized jerk index) exhibited correlations with traditional criteria-based rating scales and surgeons' years of experience. Experts completed the suturing task faster than novices. The total path distance for the right (dominant) side instrument movement was shorter for experts compared to novices. However, for the left (non-dominant) side, differences between the two groups were observed only in specific phases. The normalized jerk index for both the right and left sides was significantly lower in the expert than in the novice groups, and receiver operating characteristic analysis showed strong discriminative ability.
conclusionThe deep learning-based kinematic analytic approach for surgical instruments proves beneficial in assessing performance in microvascular anastomosis. Moreover, this methodology can be adapted for use in clinical settings.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.