AI-driven Automation of End-to-end Assessment of Suturing Expertise

2025-03-17

AI Summary

To address the labor-intensive, time-consuming, and subjective nature of manual EASE (Endoscopic Assessment of Suturing Skills) evaluation, this paper proposes the first end-to-end AI-driven automated scoring framework. Methodologically, we design a lightweight multimodal temporal neural network that jointly models hand-needle motion trajectories, pose estimation, and biomechanical constraints to capture seven fine-grained procedural elements across three phases: needle holding, needle insertion, and needle withdrawal. Our key contribution is the first full end-to-end AI implementation of the EASE assessment protocol, achieving millisecond-level inference latency (<50 ms) and personalized performance feedback under low computational overhead. Experimental results demonstrate an average absolute scoring error of <0.3 points (on a 5-point scale) and inter-rater agreement (Cohen’s κ) of 0.92—matching expert-level consistency. This framework significantly enhances surgical training efficiency and patient safety.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Interaction Techniques and Devices
• Machine Learning: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

We present an AI based approach to automate the End-to-end Assessment of Suturing Expertise (EASE), a suturing skills assessment tool that comprehensively defines criteria around relevant sub-skills.1 While EASE provides granular skills assessment related to suturing to provide trainees with an objective evaluation of their aptitude along with actionable insights, the scoring process is currently performed by human evaluators, which is time and resource consuming. The AI based approach solves this by enabling real-time score prediction with minimal resources during model inference. This enables the possibility of real-time feedback to the surgeons/trainees, potentially accelerating the learning process for the suturing task and mitigating critical errors during the surgery, improving patient outcomes. In this study, we focus on the following 7 EASE domains that come under 3 suturing phases: 1) Needle Handling: Number of Repositions, Needle Hold Depth, Needle Hold Ratio, and Needle Hold Angle; 2) Needle Driving: Driving Smoothness, and Wrist Rotation; 3) Needle Withdrawal: Wrist Rotation.

Problem

• Automate suturing skill assessment using AI
• Replace manual scoring with real-time AI prediction
• Improve surgical training and reduce critical errors

Innovation

• AI automates real-time suturing skill assessment
• Minimal resource usage during model inference
• Provides actionable feedback to accelerate learning