Browsing by Author "Halic, Tansel"

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    Endoscopic sleeve gastroplasty: stomach location and task classification for evaluation using artificial intelligence
    (Springer, 2024) Dials, James; Demirel, Doga; Sanchez-Arias, Reinaldo; Halic, Tansel; De, Suvranu; Gromski, Mark A.; Medicine, School of Medicine
    Purpose: We have previously developed grading metrics to objectively measure endoscopist performance in endoscopic sleeve gastroplasty (ESG). One of our primary goals is to automate the process of measuring performance. To achieve this goal, the repeated task being performed (grasping or suturing) and the location of the endoscopic suturing device in the stomach (Incisura, Anterior Wall, Greater Curvature, or Posterior Wall) need to be accurately recorded. Methods: For this study, we populated our dataset using screenshots and video clips from experts carrying out the ESG procedure on ex vivo porcine specimens. Data augmentation was used to enlarge our dataset, and synthetic minority oversampling (SMOTE) to balance it. We performed stomach localization for parts of the stomach and task classification using deep learning for images and computer vision for videos. Results: Classifying the stomach's location from the endoscope without SMOTE for images resulted in 89% and 84% testing and validation accuracy, respectively. For classifying the location of the stomach from the endoscope with SMOTE, the accuracies were 97% and 90% for images, while for videos, the accuracies were 99% and 98% for testing and validation, respectively. For task classification, the accuracies were 97% and 89% for images, while for videos, the accuracies were 100% for both testing and validation, respectively. Conclusion: We classified the four different stomach parts manipulated during the ESG procedure with 97% training accuracy and classified two repeated tasks with 99% training accuracy with images. We also classified the four parts of the stomach with a 99% training accuracy and two repeated tasks with a 100% training accuracy with video frames. This work will be essential in automating feedback mechanisms for learners in ESG.
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    Endoscopic Submucosal Dissection: A Cognitive Task Analysis Framework Toward Training Design
    (Springer, 2020-02) Hegde, Sudeep; Gromski, Mark A.; Halic, Tansel; Turkseven, Melih; Xia, Zhaohui; Çetinsaya, Berk; Sawhney, Mandeep S.; Jones, Daniel B.; De, Suvranu; Jackson, Cullen D.; Medicine, School of Medicine
    Background: One of the major impediments to the proliferation of endoscopic submucosal dissection (ESD) training in Western countries is the lack of sufficient experts as instructors. One way to address this gap is to develop didactic systems, such as surgical simulators, to support the role of trainers. Cognitive task analysis (CTA) has been used in healthcare for the design and improvement of surgical training programs, and therefore can potentially be used for design of similar systems for ESD. Objective: The aim of the study was to apply a CTA-based approach to identify the cognitive aspects of performing ESD, and to generate qualitative insights for training. Materials and methods: Semi-structured interviews were designed based on the CTA framework to elicit knowledge of ESD practitioners relating to the various tasks involved in the procedure. Three observations were conducted of expert ESD trainers either while they performed actual ESD procedures or at a training workshop. Interviews were either conducted over the phone or in person. Interview participants included four experts and four novices. The observation notes and interviews were analyzed for emergent qualitative themes and relationships. Results: The qualitative analysis yielded thematic insights related to four main cognition-related categories: learning goals/principles, challenges/concerns, strategies, and decision-making. The specific insights under each of these categories were systematically mapped to the various tasks inherent to the ESD procedure. Conclusions: The CTA approach was applied to identify cognitive themes related to ESD procedural tasks. Insights developed based on the qualitative analysis of interviews and observations of ESD practitioners can be used to inform the design of
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    Hierarchical task analysis of endoscopic sleeve gastroplasty
    (Springer, 2022) Dials, James; Demirel, Doga; Halic, Tansel; De, Suvranu; Ryason, Adam; Kundumadam, Shanker; Al-Haddad, Mohammad; Gromski, Mark A.; Medicine, School of Medicine
    Background: Endoscopic sleeve gastroplasty (ESG) is a minimally invasive endoscopic weight loss procedure used to treat obesity. The long-term goal of this project is to develop a Virtual Bariatric Endoscopy (ViBE) simulator for training and assessment of the ESG procedure. The objectives of this current work are to: (a) perform a task analysis of ESG and (b) create metrics to be validated in the created simulator. Methods: We performed a hierarchical task analysis (HTA) by identifying the significant tasks of the ESG procedure. We created the HTA to show the breakdown and connection of the tasks of the procedure. Utilizing the HTA and input from ESG experts, performance metrics were derived for objective measurement of the ESG procedure. Three blinded video raters analyzed seven recorded ESG procedures according to the proposed performance metrics. Results: Based on the seven videos, there was a positive correlation between total task times and total performance scores (R = 0.886, P = 0.008). Endoscopists expert were found to be more skilled in reducing the area of the stomach compared to endoscopists novice (34.6% reduction versus 9.4% reduction, P = 0.01). The mean novice performance score was significantly lower than the mean expert performance score (34.7 vs. 23.8, P = 0.047). The inter-rater reliability test showed a perfect agreement among three raters for all tasks except for the suturing task. The suturing task had a significant agreement (Inter-rater Correlation = 0.84, Cronbach's alpha = 0.88). Suturing was determined to be a critical task that is positively correlated with the total score (R = 0.962, P = 0.0005). Conclusion: The task analysis and metrics development are critical for the development of the ViBE simulator. This preliminary assessment demonstrates that the performance metrics provide an accurate assessment of the endoscopist's performance. Further validation testing and refinement of the performance metrics are anticipated.
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    Skill-level classification and performance evaluation for endoscopic sleeve gastroplasty
    (Springer Nature, 2023) Dials, James; Demirel, Doga; Sanchez‑Arias, Reinaldo; Halic, Tansel; Kruger, Uwe; De, Suvranu; Gromski, Mark A.; Medicine, School of Medicine
    Background: We previously developed grading metrics for quantitative performance measurement for simulated endoscopic sleeve gastroplasty (ESG) to create a scalar reference to classify subjects into experts and novices. In this work, we used synthetic data generation and expanded our skill level analysis using machine learning techniques. Methods: We used the synthetic data generation algorithm SMOTE to expand and balance our dataset of seven actual simulated ESG procedures using synthetic data. We performed optimization to seek optimum metrics to classify experts and novices by identifying the most critical and distinctive sub-tasks. We used support vector machine (SVM), AdaBoost, K-nearest neighbors (KNN) Kernel Fisher discriminant analysis (KFDA), random forest, and decision tree classifiers to classify surgeons as experts or novices after grading. Furthermore, we used an optimization model to create weights for each task and separate the clusters by maximizing the distance between the expert and novice scores. Results: We split our dataset into a training set of 15 samples and a testing dataset of five samples. We put this dataset through six classifiers, SVM, KFDA, AdaBoost, KNN, random forest, and decision tree, resulting in 0.94, 0.94, 1.00, 1.00, 1.00, and 1.00 accuracy, respectively, for training and 1.00 accuracy for the testing results for SVM and AdaBoost. Our optimization model maximized the distance between the expert and novice groups from 2 to 53.72. Conclusion: This paper shows that feature reduction, in combination with classification algorithms such as SVM and KNN, can be used in tandem to classify endoscopists as experts or novices based on their results recorded using our grading metrics. Furthermore, this work introduces a non-linear constraint optimization to separate the two clusters and find the most important tasks using weights.
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    A task and performance analysis of endoscopic submucosal dissection (ESD) surgery
    (Springer Verlag, 2019-02) Cetinsaya, Berk; Gromski, Mark A.; Lee, Sangrock; Xia, Zhaohui; Demirel, Doga; Halic, Tansel; Bayrak, Coskun; Jackson, Cullen; De, Suvranu; Hegde, Sudeep; Cohen, Jonah; Sawhney, Mandeep; Stavropoulos, Stavros N.; Jones, Daniel B.; Medicine, School of Medicine
    BACKGROUND: ESD is an endoscopic technique for en bloc resection of gastrointestinal lesions. ESD is a widely-used in Japan and throughout Asia, but not as prevalent in Europe or the US. The procedure is technically challenging and has higher adverse events (bleeding, perforation) compared to endoscopic mucosal resection. Inadequate training platforms and lack of established training curricula have restricted its wide acceptance in the US. Thus, we aim to develop a Virtual Endoluminal Surgery Simulator (VESS) for objective ESD training and assessment. In this work, we performed task and performance analysis of ESD surgeries. METHODS: We performed a detailed colorectal ESD task analysis and identified the critical ESD steps for lesion identification, marking, injection, circumferential cutting, dissection, intraprocedural complication management, and post-procedure examination. We constructed a hierarchical task tree that elaborates the order of tasks in these steps. Furthermore, we developed quantitative ESD performance metrics. We measured task times and scores of 16 ESD surgeries performed by four different endoscopic surgeons. RESULTS: The average time of the marking, injection, and circumferential cutting phases are 203.4 (σ: 205.46), 83.5 (σ: 49.92), 908.4 s. (σ: 584.53), respectively. Cutting the submucosal layer takes most of the time of overall ESD procedure time with an average of 1394.7 s (σ: 908.43). We also performed correlation analysis (Pearson's test) among the performance scores of the tasks. There is a moderate positive correlation (R = 0.528, p = 0.0355) between marking scores and total scores, a strong positive correlation (R = 0.7879, p = 0.0003) between circumferential cutting and submucosal dissection and total scores. Similarly, we noted a strong positive correlation (R = 0.7095, p = 0.0021) between circumferential cutting and submucosal dissection and marking scores. CONCLUSIONS: We elaborated ESD tasks and developed quantitative performance metrics used in analysis of actual surgery performance. These ESD metrics will be used in future validation studies of our VESS simulator.