Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
This study evaluates artificial intelligence (AI)-assisted videolaryngoscopy for endotracheal intubation in a simulated pediatric airway environment. Healthcare providers with varying levels of airway management experience will perform intubations on pediatric and neonatal mannequins using either AI-assisted videolaryngoscopy (larynGuide) or conventional videolaryngoscopy.
Participants will be randomized to perform intubation tasks using one of the two techniques. The primary outcome is the time required for successful intubation. Secondary outcomes include first-attempt success rate, number of attempts, airway visualization (POGO score), usability of the AI system measured by the System Usability Scale (SUS), and gaze tracking metrics evaluating user interaction with visual guidance.
This equivalence randomized controlled trial aims to determine whether AI-assisted videolaryngoscopy performs comparably to conventional videolaryngoscopy while potentially improving success rates and user experience.
Artificial intelligence is increasingly being applied in clinical medicine, including airway management. Machine vision algorithms have recently been developed to recognize airway anatomy and provide guidance during endotracheal intubation.
LarynGuide is an AI-based system designed to guide endotracheal tube placement using real-time visual prompts during videolaryngoscopy. This study aims to evaluate whether AI-assisted intubation is equivalent to conventional videolaryngoscopy in terms of time required for intubation and whether AI guidance improves success rates or usability.
This prospective randomized controlled simulation trial will recruit healthcare providers with varying levels of airway management experience. Participants will receive a short training session with the AI system and then will be randomized to perform intubations using either AI-assisted videolaryngoscopy or conventional videolaryngoscopy.
Each participant will perform intubations on both pediatric and neonatal airway mannequins in a simulation setting. Outcomes including intubation time, success rate, number of attempts, POGO score, gaze tracking metrics, and user satisfaction will be collected.
The study will be conducted at the Hospital for Sick Children Simulation Centre in Toronto, Canada.
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI-assisted videolaryngoscopy | Experimental | Participants perform simulated endotracheal intubation using videolaryngoscopy integrated with the AI guidance system |
|
| Conventional videolaryngoscopy | Active Comparator | Participants perform simulated endotracheal intubation using standard videolaryngoscopy without AI assistance. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI-assisted videolaryngoscopy | Other | Participants perform simulated endotracheal intubation using videolaryngoscopy integrated with the AI guidance system |
|
| Measure | Description | Time Frame |
|---|---|---|
| Time required for intubation | baseline, pre-intervention/procedure/surgery |
| Measure | Description | Time Frame |
|---|---|---|
| First-attempt success rate | baseline, pre-intervention/procedure/surgery | |
| POGO score | baseline, pre-intervention/procedure/surgery |
| Measure | Description | Time Frame |
|---|---|---|
| SUS score | baseline, pre-intervention/procedure/surgery | |
| Translated POGO score | baseline, pre-intervention/procedure/surgery | |
| Intubation status (good vs bad vs not started) |
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Clyde Matava | Contact | 4168137445 | clyde.matava@sickkids.ca |
Not provided
Not provided
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39149736 | Background | Nemani S, Goyal S, Sharma A, Kothari N. Artificial intelligence in pediatric airway - A scoping review. Saudi J Anaesth. 2024 Jul-Sep;18(3):410-416. doi: 10.4103/sja.sja_110_24. Epub 2024 Jun 4. | |
| 31845543 | Background | Matava C, Pankiv E, Ahumada L, Weingarten B, Simpao A. Artificial intelligence, machine learning and the pediatric airway. Paediatr Anaesth. 2020 Mar;30(3):264-268. doi: 10.1111/pan.13792. Epub 2020 Jan 2. |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Participants will be randomized to perform simulated endotracheal intubation using either AI-assisted videolaryngoscopy or conventional videolaryngoscopy. Each participant will perform intubation on both child and neonatal mannequins
Not provided
Not provided
Not provided
Not provided
| Conventional Intubation | Other | Traditional intubation |
|
| baseline, pre-intervention/procedure/surgery |
| Intubation instructions (push forward, pull back, etc) | baseline, pre-intervention/procedure/surgery |
| Laryngoscopy status & instructions (need to move right, need to move back | baseline, pre-intervention/procedure/surgery |
| Gaze duration | baseline, pre-intervention/procedure/surgery |