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Kinematic-Based Assessment of Surgical Actions in Microanastomosis

Yan Meng, Daniel Donoho, Marcelle Altshuler, Omar Arnaout

TL;DR

This paper tackles the challenge of objective, fine-grained assessment of microanastomosis skill by automating gesture segmentation and performance scoring. It introduces a modular AI framework comprising instrument tip detection/tracking, unsupervised action segmentation via a self-similarity novelty function, and supervised action-level skill classification, optimized for edge devices. On a dataset of 58 expert-rated microanastomosis videos, the method achieves frame-level action segmentation accuracy of 92.4% and skill classification accuracy of 85.5%, demonstrating close alignment with expert evaluations. The work offers real-time, interpretable feedback for microsurgical education and lays groundwork for scalable, data-driven training across high-stakes procedures.

Abstract

Proficiency in microanastomosis is a critical surgical skill in neurosurgery, where the ability to precisely manipulate fine instruments is crucial to successful outcomes. These procedures require sustained attention, coordinated hand movements, and highly refined motor skills, underscoring the need for objective and systematic methods to evaluate and enhance microsurgical training. Conventional assessment approaches typically rely on expert raters supervising the procedures or reviewing surgical videos, which is an inherently subjective process prone to inter-rater variability, inconsistency, and significant time investment. These limitations highlight the necessity for automated and scalable solutions. To address this challenge, we introduce a novel AI-driven framework for automated action segmentation and performance assessment in microanastomosis procedures, designed to operate efficiently on edge computing platforms. The proposed system comprises three main components: (1) an object tip tracking and localization module based on YOLO and DeepSORT; (2) an action segmentation module leveraging self-similarity matrix for action boundary detection and unsupervised clustering; and (3) a supervised classification module designed to evaluate surgical gesture proficiency. Experimental validation on a dataset of 58 expert-rated microanastomosis videos demonstrates the effectiveness of our approach, achieving a frame-level action segmentation accuracy of 92.4% and an overall skill classification accuracy of 85.5% in replicating expert evaluations. These findings demonstrate the potential of the proposed method to provide objective, real-time feedback in microsurgical education, thereby enabling more standardized, data-driven training protocols and advancing competency assessment in high-stakes surgical environments.

Kinematic-Based Assessment of Surgical Actions in Microanastomosis

TL;DR

This paper tackles the challenge of objective, fine-grained assessment of microanastomosis skill by automating gesture segmentation and performance scoring. It introduces a modular AI framework comprising instrument tip detection/tracking, unsupervised action segmentation via a self-similarity novelty function, and supervised action-level skill classification, optimized for edge devices. On a dataset of 58 expert-rated microanastomosis videos, the method achieves frame-level action segmentation accuracy of 92.4% and skill classification accuracy of 85.5%, demonstrating close alignment with expert evaluations. The work offers real-time, interpretable feedback for microsurgical education and lays groundwork for scalable, data-driven training across high-stakes procedures.

Abstract

Proficiency in microanastomosis is a critical surgical skill in neurosurgery, where the ability to precisely manipulate fine instruments is crucial to successful outcomes. These procedures require sustained attention, coordinated hand movements, and highly refined motor skills, underscoring the need for objective and systematic methods to evaluate and enhance microsurgical training. Conventional assessment approaches typically rely on expert raters supervising the procedures or reviewing surgical videos, which is an inherently subjective process prone to inter-rater variability, inconsistency, and significant time investment. These limitations highlight the necessity for automated and scalable solutions. To address this challenge, we introduce a novel AI-driven framework for automated action segmentation and performance assessment in microanastomosis procedures, designed to operate efficiently on edge computing platforms. The proposed system comprises three main components: (1) an object tip tracking and localization module based on YOLO and DeepSORT; (2) an action segmentation module leveraging self-similarity matrix for action boundary detection and unsupervised clustering; and (3) a supervised classification module designed to evaluate surgical gesture proficiency. Experimental validation on a dataset of 58 expert-rated microanastomosis videos demonstrates the effectiveness of our approach, achieving a frame-level action segmentation accuracy of 92.4% and an overall skill classification accuracy of 85.5% in replicating expert evaluations. These findings demonstrate the potential of the proposed method to provide objective, real-time feedback in microsurgical education, thereby enabling more standardized, data-driven training protocols and advancing competency assessment in high-stakes surgical environments.
Paper Structure (19 sections, 6 equations, 7 figures, 4 tables, 1 algorithm)

This paper contains 19 sections, 6 equations, 7 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Surgical skill assessment
  4. Instrument detection and tracking
  5. Video-based surgical activity recognition
  6. Method
  7. Instrument Tip Detection and Tracking
  8. Kinematic Change Point Detection
  9. Kinematic features self-similarity matrix
  10. Change point detection
  11. Action Clustering
  12. Microanastomosis Skill Classification
  13. Experiments
  14. Data Curation
  15. Instrument Detection Model Training
  16. ...and 4 more sections

Figures (7)

  • Figure 1: Microanastomosis skill assessment framework overview.
  • Figure 2: Instrument detection class distribution in the dataset
  • Figure 3: Normalized class confusion matrix.
  • Figure 4: Comparison of the proposed tip tracking method with YOLOv11, the top row is the YOLOv11 object detection results, the bottom row is the proposed instrument tracking and tip localization results.
  • Figure 5: Kinematic feature self-similarity matrix.
  • ...and 2 more figures