Validity of task-specific metrics for assessment in perineal proctectomy.

BACKGROUND: Perineal proctectomy is a complex procedure that requires advanced skills. Currently, there are no simulators for training in this procedure. As part of our objective of developing a virtual reality simulator, our goal was to develop and validate task-specific metrics for the assessment of performance for this procedure. We conducted a three-phase study to establish task-specific metrics, obtain expert consensus on the appropriateness of the developed metrics, and establish the discriminant validity of the developed metrics. METHODS: In phase I, we utilized hierarchical task analysis to formulate the metrics. In phase II, a survey involving expert colorectal surgeons determined the significance of the developed metrics. Phase III was aimed at establishing the discriminant validity for novices (PGY1-3) and experts (PGY4-5 and faculty). They performed a perineal proctectomy on a rectal prolapse model. Video recordings were independently assessed by two raters using global ratings and task-specific metrics for the procedure. Total scores for both metrics were computed and analyzed using the Kruskal-Wallis test. A Mann-Whitney U test with Benjamini-Hochberg correction was used to evaluate between-group differences. Spearman's rank correlation coefficient was computed to assess the correlation between global and task-specific scores. RESULTS: In phase II, a total of 23 colorectal surgeons were recruited and consensus was obtained on all the task-specific metrics. In phase III, participants (n = 22) included novices (n = 15) and experts (n = 7). There was a strong positive correlation between the global and task-specific scores (rs = 0.86; P < 0.001). Significant between-group differences were detected for both global (χ2 = 15.38; P < 0.001; df = 2) and task-specific (χ2 = 11.38; P = 0.003; df = 2) scores. CONCLUSIONS: Using a biotissue rectal prolapse model, this study documented high IRR and significant discriminant validity evidence in support of video-based assessment using task-specific metrics.

Split & Join: An Efficient Approach for Simulating Stapled Intestinal Anastomosis in Virtual Reality.

Colorectal cancer is a life-threatening disease. It is the second leading cause of cancer-related deaths in the United States. Stapled anastomosis is a rapid treatment for colorectal cancer and other intestinal diseases and has become an integral part of routine surgical practice. However, to the best of our knowledge, there is no existing work simulating intestinal anastomosis that often involves sophisticated soft tissue manipulations such as cutting and stitching. In this paper, for the first time, we propose a novel split and join approach to simulate a side-to-side stapled intestinal anastomosis in virtual reality. We mimic the intestine model using a new hybrid representation - a grid-linked particles model for physics simulation and a surface mesh for rendering. The proposed split and join operations handle the updates of both the grid-linked particles model and the surface mesh during the anastomosis procedure. The simulation results demonstrate the feasibility of the proposed approach in simulating intestine models and the side-to-side anastomosis operation.

Endoscopic sleeve gastroplasty: stomach location and task classification for evaluation using artificial intelligence.

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.

One-shot skill assessment in high-stakes domains with limited data via meta learning.

Deep Learning (DL) has achieved robust competency assessment in various high-stakes fields. However, the applicability of DL models is often hampered by their substantial data requirements and confinement to specific training domains. This prevents them from transitioning to new tasks where data is scarce. Therefore, domain adaptation emerges as a critical element for the practical implementation of DL in real-world scenarios. Herein, we introduce A-VBANet, a novel meta-learning model capable of delivering domain-agnostic skill assessment via one-shot learning. Our methodology has been tested by assessing surgical skills on five laparoscopic and robotic simulators and real-life laparoscopic cholecystectomy. Our model successfully adapted with accuracies up to 99.5 % in one-shot and 99.9 % in few-shot settings for simulated tasks and 89.7 % for laparoscopic cholecystectomy. This study marks the first instance of a domain-agnostic methodology for skill assessment in critical fields setting a precedent for the broad application of DL across diverse real-life domains with limited data.

Mechanical behavior of full-thickness burn human skin is rate-independent.

Skin tissue is recognized to exhibit rate-dependent mechanical behavior under various loading conditions. Here, we report that the full-thickness burn human skin exhibits rate-independent behavior under uniaxial tensile loading conditions. Mechanical properties, namely, ultimate tensile stress, ultimate tensile strain, and toughness, and parameters of Veronda-Westmann hyperelastic material law were assessed via uniaxial tensile tests. Univariate hypothesis testing yielded no significant difference (p > 0.01) in the distributions of these properties for skin samples loaded at three different rates of 0.3 mm/s, 2 mm/s, and 8 mm/s. Multivariate multiclass classification, employing a logistic regression model, failed to effectively discriminate samples loaded at the aforementioned rates, with a classification accuracy of only 40%. The median values for ultimate tensile stress, ultimate tensile strain, and toughness are computed as 1.73 MPa, 1.69, and 1.38 MPa, respectively. The findings of this study hold considerable significance for the refinement of burn care training protocols and treatment planning, shedding new light on the unique, rate-independent behavior of burn skin.

Distinguishing Laparoscopic Surgery Experts from Novices Using EEG Topographic Features.

The study aimed to differentiate experts from novices in laparoscopic surgery tasks using electroencephalogram (EEG) topographic features. A microstate-based common spatial pattern (CSP) analysis with linear discriminant analysis (LDA) was compared to a topography-preserving convolutional neural network (CNN) approach. Expert surgeons (N = 10) and novice medical residents (N = 13) performed laparoscopic suturing tasks, and EEG data from 8 experts and 13 novices were analysed. Microstate-based CSP with LDA revealed distinct spatial patterns in the frontal and parietal cortices for experts, while novices showed frontal cortex involvement. The 3D CNN model (ESNet) demonstrated a superior classification performance (accuracy > 98%, sensitivity 99.30%, specificity 99.70%, F1 score 98.51%, MCC 97.56%) compared to the microstate based CSP analysis with LDA (accuracy ~90%). Combining spatial and temporal information in the 3D CNN model enhanced classifier accuracy and highlighted the importance of the parietal-temporal-occipital association region in differentiating experts and novices.

Brain-behavior analysis of transcranial direct current stimulation effects on a complex surgical motor task.

Transcranial Direct Current Stimulation (tDCS) has demonstrated its potential in enhancing surgical training and performance compared to sham tDCS. However, optimizing its efficacy requires the selection of appropriate brain targets informed by neuroimaging and mechanistic understanding. Previous studies have established the feasibility of using portable brain imaging, combining functional near-infrared spectroscopy (fNIRS) with tDCS during Fundamentals of Laparoscopic Surgery (FLS) tasks. This allows concurrent monitoring of cortical activations. Building on these foundations, our study aimed to explore the multi-modal imaging of the brain response using fNIRS and electroencephalogram (EEG) to tDCS targeting the right cerebellar (CER) and left ventrolateral prefrontal cortex (PFC) during a challenging FLS suturing with intracorporeal knot tying task. Involving twelve novices with a medical/premedical background (age: 22-28 years, two males, 10 females with one female with left-hand dominance), our investigation sought mechanistic insights into tDCS effects on brain areas related to error-based learning, a fundamental skill acquisition mechanism. The results revealed that right CER tDCS applied to the posterior lobe elicited a statistically significant (q < 0.05) brain response in bilateral prefrontal areas at the onset of the FLS task, surpassing the response seen with sham tDCS. Additionally, right CER tDCS led to a significant (p < 0.05) improvement in FLS scores compared to sham tDCS. Conversely, the left PFC tDCS did not yield a statistically significant brain response or improvement in FLS performance. In conclusion, right CER tDCS demonstrated the activation of bilateral prefrontal brain areas, providing valuable mechanistic insights into the effects of CER tDCS on FLS peformance. These insights motivate future investigations into the effects of CER tDCS on error-related perception-action coupling through directed functional connectivity studies.

Deep Learning for Video-Based Assessment in Surgery.

This surgical innovation explains how applying deep neural networks could ensure the continued use of video-based assessment.