Development and Validation of a Virtual Airway Skill Trainer (VAST) Abstract Airway management is one of the most frequently performed procedures in operating rooms (OR), intensive care units (ICUs) and emergency departments (ED) to maintain air supply to the lungs. Yet, there exists no standardized curriculum or training regimen outside the age-old apprenticeship model. Hence, life- threatening scenarios, particularly those associated with the unexpected difficult airway (when an airway cannot be established using traditional methods), are seldom encountered during residency, limiting the opportunity of residents to train in the clinic. It is anticipated that a virtual reality (VR)-base trainer, with visual and haptic (touch) feedback, will be invaluable for training in airway management, allowing trainees to attain competence in a controlled environment with no risk to patients; customized learning; and real time feedback, mentoring and objective assessment. Existing VR-based simulators lack (1) realistic physics-based soft tissue modeling; (2) tissue response governed by models based on in vivo experiments; (3) realistic physical interfaces which can be easily altered to represent various external, and associated oropharyngeal anatomy of a patient known to cause difficulty with airway management [CaPe10] e.g., receding jaw, excessive soft tissue and large neck circumference; (4) cognitive skill training related to higher level mental functions associated with workload management, planning, coordination, and decision-making based on level of difficulty, in addition to psychomotor (hand-eye coordination and motor skills) skill training; and (5) clinical validation. The goal of this projec is to overcome these barriers and design, develop and validate the Virtual Airway Skill Trainer (VAST). A multidisciplinary team has been assembled to achieve the following Specific Aims: (SA1) Design and develop the Virtual Airway Skill Trainer (VAST) platform. Specifically, we will develop (1) physics-based computational models of human anatomy based upon in vivo experimental studies; (2) an immersive 3D high definition (HD) head mounted display (HMD) system to represent the clinical environment (OR/ICU/ED); and (3) an innovative bi-manual force feedback hardware interface with tactile matrix gloves that allows representation of various patient anatomies associated with the difficult airway. (SA 2) Develop simulation scenarios for endotracheal intubation (ETI) and cricothyrotomy (CCT) procedures within the VAST by integrating the computational models and experimental data generated in SA1. VAST will include real time feedback identifying errors; visual, auditory and haptic cues to guide the trainee; display of physiological consequence of complications; effects of alternate procedures and devices as well as automatic real time assessment of skill. (SA 3) Establish the validity of the VAST as a training tool by conducting experiments at Beth Israel Deaconess Medical Center (BIDMC) and Massachusetts General Hospital (MGH) in Boston to ensure that appropriate skills are being learnt on the VAST and performance measured on the VAST reflect the technical skills they intend to measure.
The goal of this research is to develop and validate a comprehensive computer-based technology that will allow medical trainees to practice their airway management skills on computer-based models. Clinical procedures and techniques learnt and perfected in this risk-free manner before application to patients, will translate to fewr errors, reduced patient morbidity and improved patient outcomes resulting in reduced complications and treatment costs.
| Nemani, Arun; Ahn, Woojin; Cooper, Clairice et al. (2018) Convergent validation and transfer of learning studies of a virtual reality-based pattern cutting simulator. Surg Endosc 32:1265-1272 |
| Karaki, Wafaa; Rahul; Lopez, Carlos A et al. (2018) A Two-Scale Model of Radio-Frequency Electrosurgical Tissue Ablation. Comput Mech 62:803-814 |
| Cetinsaya, Berk; Gromski, Mark A; Lee, Sangrock et al. (2018) A task and performance analysis of endoscopic submucosal dissection (ESD) surgery. Surg Endosc : |
| Han, Zhongqing; Rahul, Suvranu De (2018) A Multiphysics Model for Radiofrequency Activation of Soft Hydrated Tissues. Comput Methods Appl Mech Eng 337:527-548 |
| Demirel, Doga; Yu, Alexander; Baer-Cooper, Seth et al. (2017) Generative Anatomy Modeling Language (GAML). Int J Med Robot 13: |
| Qi, Di; Panneerselvam, Karthikeyan; Ahn, Woojin et al. (2017) Virtual interactive suturing for the Fundamentals of Laparoscopic Surgery (FLS). J Biomed Inform 75:48-62 |
| Suvranu De, Rahul (2017) A multi-physics model for ultrasonically activated soft tissue. Comput Methods Appl Mech Eng 314:71-84 |
| Demirel, Doga; Butler, Kathryn L; Halic, Tansel et al. (2016) A hierarchical task analysis of cricothyroidotomy procedure for a virtual airway skills trainer simulator. Am J Surg 212:475-84 |
| Sankaranarayanan, Ganesh; Li, Baichun; Miller, Amie et al. (2016) Face validation of the Virtual Electrosurgery Skill Trainer (VEST©). Surg Endosc 30:730-8 |
| Sankaranarayanan, Ganesh; Li, Baichun; Manser, Kelly et al. (2016) Face and construct validation of a next generation virtual reality (Gen2-VR) surgical simulator. Surg Endosc 30:979-85 |
Showing the most recent 10 out of 31 publications
