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Scopus Q: Q1Subject: Surgery

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    Article
    Citation - WoS: 8
    Citation - Scopus: 10
    Evaluation of Ten Open-Source Eye-Movement Classification Algorithms in Simulated Surgical Scenarios
    (Ieee-inst Electrical Electronics Engineers inc, 2019) Dalveren, Gonca Gokce Menekse; Cagiltay, Nergiz Ercil
    Despite providing several insights into visual attention and evidence regarding certain brain states and psychological functions, classifying eye movements is a highly demanding process. Currently, there are several algorithms to classify eye movement events which use different approaches. However, to date, only a limited number of studies have assessed these algorithms under specific conditions, such as those required for surgical training programmes. This study presents an investigation of ten open-source eye-movement classification algorithms using the Eye Tribe eye-tracker. The algorithms were tested on the eye-movement records obtained from 23 surgical residents, who performed computer-based surgical simulation tasks under different hand conditions. The aim was to offer data for the improvement of surgical training programmes. According to the results, due to the different classification methods and default threshold values, the ten algorithms produced different results. Considering the fixation duration, the only common event for all of the investigated algorithms, the binocular-individual threshold (BIT) algorithm resulted in a different clustering compared to the other algorithms. Based on the other set of common events, three clusters were determined by eight algorithms (except BIT and event detection (ED)), distinguishing dispersion-based, velocity-based and modified versions of velocity-based algorithms. Accordingly, it was concluded that dispersion-based and velocity-based algorithms provided different results. Additionally, as it individually specifies the threshold values for the eye-movement data, when there is no consensus about the threshold values to be set, the BIT algorithm can be selected. Especially for such cases like simulation-based surgical skill-training, the use of individualised threshold values in the BIT algorithm can be more beneficial in classifying the raw eye data and thus evaluating the individual progress levels of trainees based on their eye movement behaviours. In conclusion, the threshold values had a critical effect on the algorithm results. Since default values may not always be suitable for the unique features of different data sets, guidelines should be developed to indicate how the threshold values are set for each algorithm.
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    Review
    Citation - WoS: 7
    Citation - Scopus: 7
    A Systematic Review on Classification and Assessment of Surgical Skill Levels for Simulation-Based Training Programs
    (Elsevier Ireland Ltd, 2023) Tonbul, Gokcen; Topalli, Damla; Cagiltay, Nergiz Ercil
    Background: Nowadays, advances in medical informatics have made minimally invasive surgery (MIS) procedures the preferred choice. However, there are several problems with the education programs in terms of surgical skill acquisition. For instance, defining and objectively measuring surgical skill levels is a challenging process. Accordingly, the aim of this study is to conduct a literature review for an investigation of the current approaches for classifying the surgical skill levels and for identifying the skill training tools and measurement methods.Materials and Methods: In this research, a search is conducted and a corpus is created. Exclusion and inclusion criteria are applied by limiting the number of articles based on surgical education, training approximations, hand movements, and endoscopic or laparoscopic operations. To satisfy these criteria, 57 articles are included in the corpus of this study.Results: Currently used surgical skill assessment approaches have been summarized. Results show that various classification approaches for the surgical skill level definitions are being used. Besides, many studies are con-ducted by omitting particularly important skill levels in between. Additionally, some inconsistencies are also identified across the skill level classification studies.Conclusion: In order to improve the benefits of simulation-based training programs, a standardized interdisci-plinary approach should be developed. For this reason, specific to each surgical procedure, the required skills should be identified. Additionally, appropriate measures for assessing these skills, which can be defined in simulation-based MIS training environments, should be refined. Finally, the skill levels gained during the developmental stages of these skills, with their threshold values referencing the identified measures, should be redefined in a standardized manner.