Augmented Reality Future Step Visualization for Robust Surgical Telementoring.


Journal

Simulation in healthcare : journal of the Society for Simulation in Healthcare
ISSN: 1559-713X
Titre abrégé: Simul Healthc
Pays: United States
ID NLM: 101264408

Informations de publication

Date de publication:
Feb 2019
Historique:
pubmed: 6 11 2018
medline: 29 5 2019
entrez: 6 11 2018
Statut: ppublish

Résumé

Surgical telementoring connects expert mentors with trainees performing urgent care in austere environments. However, such environments impose unreliable network quality, with significant latency and low bandwidth. We have developed an augmented reality telementoring system that includes future step visualization of the medical procedure. Pregenerated video instructions of the procedure are dynamically overlaid onto the trainee's view of the operating field when the network connection with a mentor is unreliable. Our future step visualization uses a tablet suspended above the patient's body, through which the trainee views the operating field. Before trainee use, an expert records a "future library" of step-by-step video footage of the operation. Videos are displayed to the trainee as semitransparent graphical overlays. We conducted a study where participants completed a cricothyroidotomy under telementored guidance. Participants used one of two telementoring conditions: conventional telestrator or our system with future step visualization. During the operation, the connection between trainee and mentor was bandwidth throttled. Recorded metrics were idle time ratio, recall error, and task performance. Participants in the future step visualization condition had 48% smaller idle time ratio (14.5% vs. 27.9%, P < 0.001), 26% less recall error (119 vs. 161, P = 0.042), and 10% higher task performance scores (rater 1 = 90.83 vs. 81.88, P = 0.008; rater 2 = 88.54 vs. 79.17, P = 0.042) than participants in the telestrator condition. Future step visualization in surgical telementoring is an important fallback mechanism when trainee/mentor network connection is poor, and it is a key step towards semiautonomous and then completely mentor-free medical assistance systems.

Identifiants

pubmed: 30395078
doi: 10.1097/SIH.0000000000000334
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

59-66

Auteurs

Daniel S Andersen (DS)

From the Departments of Computer Science (D.S.A., V.S.P.) and Industrial Engineering (M.E.C., E.J.R.M., G.T.G., J.P.W.), Purdue University, West Lafayette; and Indiana University, School of Medicine (B.M., S.M., B.L.Z.), Bloomington, IN.

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