Quantitative Outcome-Oriented Assessment of Microsurgical Anastomosis

TL;DR

This paper introduces a quantitative, image-processing-based framework for objectively assessing microsurgical anastomosis quality, improving reliability and efficiency over subjective methods.

Contribution

It presents a novel geometric modeling approach that automates error detection and scoring, advancing microsurgical training and assessment protocols.

Findings

Geometric metrics replicate expert scoring effectively.

The framework enhances assessment reliability and efficiency.

Uses three datasets from hospital participants.

Abstract

Microsurgical anastomosis demands exceptional dexterity and visuospatial skills, underscoring the importance of comprehensive training and precise outcome assessment. Currently, methods such as the outcome-oriented anastomosis lapse index are used to evaluate this procedure. However, they often rely on subjective judgment, which can introduce biases that affect the reliability and efficiency of the assessment of competence. Leveraging three datasets from hospitals with participants at various levels, we introduce a quantitative framework that uses image-processing techniques for objective assessment of microsurgical anastomoses. The approach uses geometric modeling of errors along with a detection and scoring mechanism, enhancing the efficiency and reliability of microsurgical proficiency assessment and advancing training protocols. The results show that the geometric metrics effectively replicate expert raters' scoring for the errors considered in this work.