An AI Framework for Microanastomosis Motion Assessment

Proficiency in microanastomosis is a fundamental competency across multiple microsurgical disciplines. These procedures demand exceptional precision and refined technical skills, making effective, standardized assessment methods essential. Traditionally, the evaluation of microsurgical techniques has relied heavily on the subjective judgment of expert raters. They are inherently constrained by limitations such as inter-rater variability, lack of standardized evaluation criteria, susceptibility to cognitive bias, and the time-intensive nature of manual review. These shortcomings underscore the urgent need for an objective, reliable, and automated system capable of assessing microsurgical performance with consistency and scalability. To bridge this gap, we propose a novel AI framework for the automated assessment of microanastomosis instrument handling skills. The system integrates four core components: (1) an instrument detection module based on the You Only Look Once (YOLO) architecture; (2) an instrument tracking module developed from Deep Simple Online and Realtime Tracking (DeepSORT); (3) an instrument tip localization module employing shape descriptors; and (4) a supervised classification module trained on expert-labeled data to evaluate instrument handling proficiency. Experimental results demonstrate the effectiveness of the framework, achieving an instrument detection precision of 97%, with a mean Average Precision (mAP) of 96%, measured by Intersection over Union (IoU) thresholds ranging from 50% to 95% (mAP50-95).

Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets

Autonomous Driving (AD), the area of robotics with the greatest potential impact on society, has gained a lot of momentum in the last decade. As a result of this, the number of datasets in AD has increased rapidly. Creators and users of datasets can benefit from a better understanding of developments in the field. While scientometric analysis has been conducted in other fields, it rarely revolves around datasets. Thus, the impact, attention, and influence of datasets on autonomous driving remains a rarely investigated field. In this work, we provide a scientometric analysis for over 200 datasets in AD. We perform a rigorous evaluation of relations between available metadata and citation counts based on linear regression. Subsequently, we propose an Influence Score to assess a dataset already early on without the need for a track-record of citations, which is only available with a certain delay.