Automated strabismus detection and classification using deep learning analysis of facial images.

Strabismus, or eye misalignment, is a common condition affecting individuals of all ages. Early detection and accurate classification are essential for proper treatment and avoiding long-term complications. This research presents a new deep-learning-based approach for automatically identifying and classifying strabismus from facial images.

Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task.

Virtual reality surgical simulators have facilitated surgical education by providing a safe training environment. Electroencephalography (EEG) has been employed to assess neuroelectric activity during surgical performance. Machine learning (ML) has been applied to analyze EEG data split into frequency bands.

Dietary nutrients' intake and sleep quality in peritoneal dialysis patients.

Sleep disturbances are common in dialysis patients. However, there is a lack of information on nutritional determinants of sleep disorders in dialysis patients. The objective of the current study was to investigate the association between nutrients' intake and sleep quality in peritoneal dialysis patients.

Stain transfer using Generative Adversarial Networks and disentangled features.

With the digitization of histopathology, machine learning algorithms have been developed to help pathologists. Color variation in histopathology images degrades the performance of these algorithms. Many models have been proposed to resolve the impact of color variation and transfer histopathology images to a single stain style.

Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task.

This study outlines the first investigation of application of machine learning to distinguish "skilled" and "novice" psychomotor performance during a virtual reality (VR) brain tumor resection task. Tumor resection task participants included 23 neurosurgeons and senior neurosurgery residents as the "skilled" group and 92 junior neurosurgery residents and medical students as the "novice" group. The task involved removing a series of virtual brain tumors without causing injury to surrounding tissue.

Brain activity during time to contact estimation: an EEG study.

Understanding the neural mechanisms associated with time to contact (TTC) estimation is an intriguing but challenging task. Despite the importance of TTC estimation in our everyday life, few studies have been conducted on it, and there are still a lot of unanswered questions and unknown aspects of this issue. In this study, we intended to address one of these unknown aspects.

Drift-diffusion explains response variability and capacity for tracking objects.

Being able to track objects that surround us is key for planning actions in dynamic environments. However, rigorous cognitive models for tracking of one or more objects are currently lacking. In this study, we asked human subjects to judge the time to contact (TTC) a finish line for one or two objects that became invisible shortly after moving.

Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation.

Importance: Despite advances in the assessment of technical skills in surgery, a clear understanding of the composites of technical expertise is lacking. Surgical simulation allows for the quantitation of psychomotor skills, generating data sets that can be analyzed using machine learning algorithms.

Objective: To identify surgical and operative factors selected by a machine learning algorithm to accurately classify participants by level of expertise in a virtual reality surgical procedure.

Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation.

Objective: Virtual reality simulators track all movements and forces of simulated instruments, generating enormous datasets which can be further analyzed with machine learning algorithms. These advancements may increase the understanding, assessment and training of psychomotor performance. Consequently, the application of machine learning techniques to evaluate performance on virtual reality simulators has led to an increase in the volume and complexity of publications which bridge the fields of computer science, medicine, and education.

Spiral waves in externally excited neuronal network: Solvable model with a monotonically differentiable magnetic flux.

Spiral waves are particular spatiotemporal patterns connected to specific phase singularities representing topological wave dislocations or nodes of zero amplitude, witnessed in a wide range of complex systems such as neuronal networks. The appearance of these waves is linked to the network structure as well as the diffusion dynamics of its blocks. We report a novel form of the Hindmarsh-Rose neuron model utilized as a square neuronal network, showing the remarkable multistructure of dynamical patterns ranging from characteristic spiral wave domains of spatiotemporal phase coherence to regions of hyperchaos.

Intravascular optical coherence tomography to validate finite-element simulation of angioplasty balloon inflation.

Concrete methods are lacking to examine angioplasty simulation results. For the first time, we explored the application of intravascular optical coherence tomography (IVOCT) to experimentally validate results obtained from finite-element simulation of angioplasty balloon deployment. In order to simulate each experimental scenario, IVOCT images were used to create initial geometrical models for the balloon and the phantoms.

Development of a performance model for virtual reality tumor resections.

Objective: Previous work from the authors has shown that hand ergonomics plays an important role in surgical psychomotor performance during virtual reality brain tumor resections. In the current study they propose a hypothetical model that integrates the human and task factors at play during simulated brain tumor resections to better understand the hand ergonomics needed for optimal safety and efficiency. They hypothesize that 1) experts (neurosurgeons), compared to novices (residents and medical students), spend a greater proportion of their time in direct contact with critical tumor areas; 2) hand ergonomic conditions (most favorable to unfavorable) prompt participants to adapt in order to optimize tumor resection; and 3) hand ergonomic adaptation is acquired with increasing expertise.

Virtual Reality Tumor Resection: The Force Pyramid Approach.

Background: The force pyramid is a novel visual representation allowing spatial delineation of instrument force application during surgical procedures. In this study, the force pyramid concept is employed to create and quantify dominant hand, nondominant hand, and bimanual force pyramids during resection of virtual reality brain tumors.

Objective: To address 4 questions: Do ergonomics and handedness influence force pyramid structure? What are the differences between dominant and nondominant force pyramids? What is the spatial distribution of forces applied in specific tumor quadrants? What differentiates "expert" and "novice" groups regarding their force pyramids?

Methods: Using a simulated aspirator in the dominant hand and a simulated sucker in the nondominant hand, 6 neurosurgeons and 14 residents resected 8 different tumors using the CAE NeuroVR virtual reality neurosurgical simulation platform (CAE Healthcare, Montréal, Québec and the National Research Council Canada, Boucherville, Québec).

Automaticity of Force Application During Simulated Brain Tumor Resection: Testing the Fitts and Posner Model.

Objective: The Fitts and Posner model of motor learning hypothesized that with deliberate practice, learners progress through stages to an autonomous phase of motor ability. To test this model, we assessed the automaticity of neurosurgeons, senior residents, and junior residents when operating on 2 identical tumors using the NeuroVR virtual reality simulation platform.

Design: Participants resected 9 identical simulated tumors on 2 occasions (total = 18 resections).

The force pyramid: a spatial analysis of force application during virtual reality brain tumor resection.

OBJECTIVE Virtual reality simulators allow development of novel methods to analyze neurosurgical performance. The concept of a force pyramid is introduced as a Tier 3 metric with the ability to provide visual and spatial analysis of 3D force application by any instrument used during simulated tumor resection. This study was designed to answer 3 questions: 1) Do study groups have distinct force pyramids? 2) Do handedness and ergonomics influence force pyramid structure? 3) Are force pyramids dependent on the visual and haptic characteristics of simulated tumors? METHODS Using a virtual reality simulator, NeuroVR (formerly NeuroTouch), ultrasonic aspirator force application was continually assessed during resection of simulated brain tumors by neurosurgeons, residents, and medical students.

Bimanual Psychomotor Performance in Neurosurgical Resident Applicants Assessed Using NeuroTouch, a Virtual Reality Simulator.

Objective: Current selection methods for neurosurgical residents fail to include objective measurements of bimanual psychomotor performance. Advancements in computer-based simulation provide opportunities to assess cognitive and psychomotor skills in surgically naive populations during complex simulated neurosurgical tasks in risk-free environments. This pilot study was designed to answer 3 questions: (1) What are the differences in bimanual psychomotor performance among neurosurgical residency applicants using NeuroTouch? (2) Are there exceptionally skilled medical students in the applicant cohort? and (3) Is there an influence of previous surgical exposure on surgical performance?

Design: Participants were instructed to remove 3 simulated brain tumors with identical visual appearance, stiffness, and random bleeding points.

Impact of acute stress on psychomotor bimanual performance during a simulated tumor resection task.

OBJECTIVE Severe bleeding during neurosurgical operations can result in acute stress affecting the bimanual psychomotor performance of the operator, leading to surgical error and an adverse patient outcome. Objective methods to assess the influence of acute stress on neurosurgical bimanual psychomotor performance have not been developed. Virtual reality simulators, such as NeuroTouch, allow the testing of acute stress on psychomotor performance in risk-free environments.

Neurosurgical Assessment of Metrics Including Judgment and Dexterity Using the Virtual Reality Simulator NeuroTouch (NAJD Metrics).

Advances in computer-based technology has created a significant opportunity for implementing new training paradigms in neurosurgery focused on improving skill acquisition, enhancing procedural outcome, and surgical skills assessment. NeuroTouch is a computer-based virtual reality system that can generate output data known as metrics from operator performance during simulated brain tumor resection. These measures of quantitative assessment are used to track and compare psychomotor performance during simulated operative procedures.

Prevention of postoperative nausea and vomiting with a subhypnotic dose of Propofol in patients undergoing lower abdominal surgery: A prospective, randomized, double-blind study.

Background: Postoperative nausea and vomiting (PONV) is a common complication after general anesthesia in patients undergoing elective lower abdominal surgery. We aimed to compare the effect of a sub hypnotic dose of Propofol in the prevention of PONV after lower abdominal surgery with that of the conventional antiemetic drug Metoclopramide.

Materials And Methods: In this prospective, randomized, double-blind, placebo-controlled study, 104 patients with American Society of Anesthesiologists (ASA) class I or II status, aged 18-65 years, and undergoing elective lower abdominal surgery were randomized to one of four groups (n = 26 each).

Proficiency performance benchmarks for removal of simulated brain tumors using a virtual reality simulator NeuroTouch.

Objective: Assessment of neurosurgical technical skills involved in the resection of cerebral tumors in operative environments is complex. Educators emphasize the need to develop and use objective and meaningful assessment tools that are reliable and valid for assessing trainees' progress in acquiring surgical skills. The purpose of this study was to develop proficiency performance benchmarks for a newly proposed set of objective measures (metrics) of neurosurgical technical skills performance during simulated brain tumor resection using a new virtual reality simulator (NeuroTouch).

Assessing bimanual performance in brain tumor resection with NeuroTouch, a virtual reality simulator.

Background: Validated procedures to objectively measure neurosurgical bimanual psychomotor skills are unavailable. The NeuroTouch simulator provides metrics to determine bimanual performance, but validation is essential before implementation of this platform into neurosurgical training, assessment, and curriculum development.

Objective: To develop, evaluate, and validate neurosurgical bimanual performance metrics for resection of simulated brain tumors with NeuroTouch.

Awareness and apgar score in elective Cesarean section under general anesthesia with propofol or Isoflurane: A prospective, randomized, double-blinded clinical trial study.

Background: Awareness is a postoperative recall of events experienced under general anesthesia. In this study, we compared the incidence of awareness between two routine methods used, inhalation (Isoflurane) and intravenous protocol (Propofol), in elective Cesarean section, and also evaluated the effect of these two different methods on the apgar score of newborns.

Materials And Methods: In this prospective, clinical trial study, 90 pregnant women candidates for elective Cesarean section were randomly enrolled, after taking written consent.

Neurosurgical virtual reality simulation metrics to assess psychomotor skills during brain tumor resection.

Purpose: Virtual reality simulator technology together with novel metrics could advance our understanding of expert neurosurgical performance and modify and improve resident training and assessment. This pilot study introduces innovative metrics that can be measured by the state-of-the-art simulator to assess performance. Such metrics cannot be measured in an operating room and have not been used previously to assess performance.

Intravascular optical coherence tomography to characterize tissue deformation during angioplasty: preliminary experiments with artery phantoms.

We explored the potential of intravascular optical coherence tomography (IVOCT) to assess deformation during angioplasty balloon inflation. Using a semi-compliant balloon and artery phantoms, we considered two experimental scenarios. The goal for the first scenario was to investigate if variation in the elasticity of the structure surrounding the balloon could be sensed by IVOCT monitoring.