Endoscopic submucosal dissection: a cognitive task analysis framework toward training design.

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 ESD training systems, such as virtual reality-based simulators.

Partition-based optimization model for generative anatomy modeling language (POM-GAML).

BACKGROUND: This paper presents a novel approach for Generative Anatomy Modeling Language (GAML). This approach automatically detects the geometric partitions in 3D anatomy that in turn speeds up integrated non-linear optimization model in GAML for 3D anatomy modeling with constraints (e.g. joints). This integrated non-linear optimization model requires the exponential execution time. However, our approach effectively computes the solution for non-linear optimization model and reduces computation time from exponential to linear time. This is achieved by grouping the 3D geometric constraints into communities. METHODS: Various community detection algorithms (k-means clustering, Clauset Newman Moore, and Density-Based Spatial Clustering of Applications with Noise) were used to find communities and partition the non-linear optimization problem into sub-problems. GAML was used to create a case study for 3D shoulder model to benchmark our approach with up to 5000 constraints. RESULTS: Our results show that the computation time was reduced from exponential time to linear time and the error rate between the partitioned and non-partitioned approach decreases with the increasing number of constraints. For the largest constraint set (5000 constraints), speed up was over 2689-fold whereas error was computed as low as 2.2%. CONCLUSION: This study presents a novel approach to group anatomical constraints in 3D human shoulder model using community detection algorithms. A case study for 3D modeling for shoulder models developed for arthroscopic rotator cuff simulation was presented. Our results significantly reduced the computation time in conjunction with a decrease in error using constrained optimization by linear approximation, non-linear optimization solver.

A task and performance analysis of endoscopic submucosal dissection (ESD) surgery.

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.

A partition-based optimization model and its performance benchmark for Generative Anatomy Modeling Language.

BACKGROUND: This paper presents a novel iterative approach and rigorous accuracy testing for geometry modeling language - a Partition-based Optimization Model for Generative Anatomy Modeling Language (POM-GAML). POM-GAML is designed to model and create anatomical structures and their variations by satisfying any imposed geometric constraints using a non-linear optimization model. Model partitioning of POM-GAML creates smaller sub-problems of the original model to reduce the exponential execution time required to solve the constraints in linear time with a manageable error. METHOD: We analyzed our model concerning the iterative approach and graph parameters for different constraint hierarchies. The iteration was used to reduce the error for partitions and solve smaller sub-problems generated by various clustering/community detection algorithms. We empirically tested our model with eleven graph parameters. Graphs for each parameter with increasing constraint sets were generated to evaluate the accuracy of our method. RESULTS: The average decrease in normalized error with respect to the original problem using cluster/community detection algorithms for constraint sets was above 63.97%. The highest decrease in normalized error after five iterations for the constraint set of 3900 was 70.31%, while the lowest decrease for the constraint set of 3000 was with 63.97%. Pearson correlation analysis between graph parameters and normalized error was carried out. We identified that graph parameters such as diameter, average eccentricity, global efficiency, and average local efficiency showed strong correlations to the normalized error. CONCLUSIONS: We observed that iteration monotonically decreases the error in all experiments. Our iteration results showed decreased normalized error using the partitioned constrained optimization by linear approximation to the non-linear optimization model.