Abstract
Accurate and robust assessment of non-traditional approaches used for training students and professionals in improving laparoscopic surgical skills has been attracting many research studies recently. Such assessment is particularly critical with the recent advances related to virtual environments and AI tools in addressing the need to expand the education and training in the medical domains. Network models and population analysis methods have been identified as excellent approaches in providing the much-needed assessment. This study aims at further advancing the surgical skill assessment by introducing a comparative approach to threshold optimization in analyzing the network models. While the majority of network methods often on arbitrary or hard thresholds for network construction and analysis, this research explores the efficacy of network-based parameters for identifying key elements and clusters in extracting useful information from the constructed networks. We report the positive impact of using network structural parameters, such as edge betweenness and modularity, to conduct robust analysis of the assessment networks. In this work, we employ electromyography (EMG) data and the NASA Task Load Index (NASA-TLX) scores for comprehensive skill evaluation. We present a case study that highlights the advantage of selecting thresholds based on the highest edge betweenness associated with the obtained assessment networks. This proposed approach method proved to be more effective in identifying participants who exhibit significant learning progression, aligning their muscle activation patterns closely with top performers. We demonstrate that optimizing thresholds through edge betweenness offers a more accurate visualization and assessment of skill acquisition in laparoscopic surgery training.