Validity of a virtual reality-based straight coloanal anastomosis simulator.

Purpose: Current training methods for surgical trainees are inadequate because they are costly, low-fidelity, or have a low skill ceiling. This work aims to expand available virtual reality training options by developing a VR trainer for straight coloanal anastomosis (SCA), one of the Colorectal Objective Structured Assessment of Technical Skills (COSATS) tasks.

Methods: We developed a VR-based SCA simulator to evaluate trainees based on their performance.

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.

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.

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.

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.

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.

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.

Specular Reflection Removal for 3D Reconstruction of Tissues using Endoscopy Videos.

Endoscopy is widely employed for diagnostic examination of the interior of organs and body cavities and numerous surgical interventions. Still, the inability to correlate individual 2D images with 3D organ morphology limits its applications, especially in intra-operative planning and navigation, disease physiology, cancer surveillance, etc. As a result, most endoscopy videos, which carry enormous data potential, are used only for real-time guidance and are discarded after collection.

Assessment of Surgical Tasks Using Neuroimaging Dataset (ASTaUND).

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool for studying brain activity in mobile subjects. Open-access fNIRS datasets are limited to simple and/or motion-restricted tasks. Here, we report a fNIRS dataset acquired on mobile subjects performing Fundamentals of Laparoscopic Surgery (FLS) tasks in a laboratory environment.

What are essential laparoscopic skills these days? Results of the SAGES Fundamentals of Laparoscopic Surgery (FLS) Committee technical skills survey.

Introduction: The Fundamentals of Laparoscopic Surgery (FLS) program tests basic knowledge and skills required to perform laparoscopic surgery. Educational experiences in laparoscopic training and development of associated competencies have evolved since FLS inception, making it important to review the definition of fundamental laparoscopic skills. The Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) assigned an FLS Technical Skills Working Group to characterize technical skills used in basic laparoscopic surgery in current practice contexts and their possible application to future FLS tests.

Changes in functional neuroimaging measures as novices gain proficiency on the fundamentals of laparoscopic surgery suturing task.

Significance: As trainees practice fundamental surgical skills, they typically rely on performance measures such as time and errors, which are limited in their sensitivity.

Aim: The goal of our study was to evaluate the use of portable neuroimaging measures to map the neural processes associated with learning basic surgical skills.

Approach: Twenty-one subjects completed 15 sessions of training on the fundamentals of laparoscopic surgery (FLS) suture with intracorporeal knot-tying task in a box trainer.

A statistical machine learning approach linking molecular conformational changes to altered mechanical characteristics of skin due to thermal injury.

This article develops statistical machine learning models to predict the mechanical properties of skin tissue subjected to thermal injury based on the Raman spectra associated with conformational changes of the molecules in the burned tissue. Ex vivo porcine skin tissue samples were exposed to controlled burn conditions at 200 °F for five different durations: (i) 10s, (ii) 20s, (iii) 30s, (iv) 40s, and (v) 50s. For each burn condition, Raman spectra of wavenumbers 500-2000 cm were measured from the tissue samples, and tensile testing on the same samples yielded their material properties, including, ultimate tensile strain, ultimate tensile stress, and toughness.

Skill-level classification and performance evaluation for endoscopic sleeve gastroplasty.

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.

Video-based formative and summative assessment of surgical tasks using deep learning.

To ensure satisfactory clinical outcomes, surgical skill assessment must be objective, time-efficient, and preferentially automated-none of which is currently achievable. Video-based assessment (VBA) is being deployed in intraoperative and simulation settings to evaluate technical skill execution. However, VBA is manual, time-intensive, and prone to subjective interpretation and poor inter-rater reliability.

Validation of a Virtual Simulator With Haptic Feedback for Endotracheal Intubation Training.

Introduction: Endotracheal intubation (ETI) is a procedure that varies in difficulty because of patient characteristics and clinical conditions. Existing physical simulators do not encompass these variations. The Virtual Airway Skills Trainer for Endotracheal Intubation (VAST-ETI) was developed to provide different patient characteristics and high-fidelity haptic feedback to improve training.

Error-related brain state analysis using electroencephalography in conjunction with functional near-infrared spectroscopy during a complex surgical motor task.

Error-based learning is one of the basic skill acquisition mechanisms that can be modeled as a perception-action system and investigated based on brain-behavior analysis during skill training. Here, the error-related chain of mental processes is postulated to depend on the skill level leading to a difference in the contextual switching of the brain states on error commission. Therefore, the objective of this paper was to compare error-related brain states, measured with multi-modal portable brain imaging, between experts and novices during the Fundamentals of Laparoscopic Surgery (FLS) "suturing and intracorporeal knot-tying" task (FLS complex task)-the most difficult among the five psychomotor FLS tasks.

Portable neuroimaging differentiates novices from those with experience for the Fundamentals of Laparoscopic Surgery (FLS) suturing with intracorporeal knot tying task.

Background: The goal of this study was to compare the brain activation patterns of experienced and novice individuals when performing the Fundamentals of Laparoscopic Surgery (FLS) suture with intracorporeal knot tying task, which requires bimanual motor control.

Methods: Twelve experienced and fourteen novice participants completed this cross-sectional observational study. Participants performed three repetitions of the FLS suture with intracorporeal knot tying task in a standard box trainer.

Objective metrics for hand-sewn bowel anastomoses can differentiate novice from expert surgeons.

Background: Assessing performance automatically in a virtual reality trainer or from recorded videos is advantageous but needs validated objective metrics. The purpose of this study is to obtain expert consensus and validate task-specific metrics developed for assessing performance in double-layered end-to-end anastomosis.

Materials And Methods: Subjects were recruited into expert (PGY 4-5, colorectal surgery residents, and attendings) and novice (PGY 1-3) groups.

Development and Validation of Task-Specific Metrics for the Assessment of Linear Stapler-Based Small Bowel Anastomosis.

Introduction: Task-specific metrics facilitate the assessment of surgeon performance. This 3-phased study was designed to (1) develop task-specific metrics for stapled small bowel anastomosis, (2) obtain expert consensus on the appropriateness of the developed metrics, and (3) establish its discriminant validity.

Methods: In Phase I, a hierarchical task analysis was used to develop the metrics.

Error related fNIRS-EEG microstate analysis during a complex surgical motor task.

Fundamentals of Laparoscopic Surgery (FLS) is a standard education and training module with a set of basic surgical skills. During surgical skill acquisition, novices need to learn from errors due to perturbations in their performance which is one of the basic principles of motor skill acquisition. This study on thirteen healthy novice medical students and nine expert surgeons aimed to capture the brain state during error epochs using multimodal brain imaging by combining functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG).