AI-Integrated 3D-Printed Videolaryngoscope for Orotracheal Intubation in Critically Ill Patients

Summary

Background: Orotracheal intubation is an essential procedure in critically ill patients requiring ventilatory support, and the use of videolaryngoscopy is recommended as the gold standard in clinical practice. In recent years, the development of videolaryngoscopes through additive manufacturing has demonstrated lower production costs, and their integration with artificial intelligence (AI) has shown promising results in improving procedural success and safety.

Aim: To evaluate the clinical performance and usability of a videolaryngoscope developed through additive manufacturing and integrated with artificial intelligence (AI) and computer vision, compared with a commercially available gold-standard videolaryngoscope.

Methods: This is a multicenter, randomized, controlled, superiority Phase I/II clinical trial with a single-blind design. A total of 120 critically ill patients admitted to intensive care units at four hospitals in southern Brazil will be enrolled. Participants will be individually randomized in a 1:1 allocation ratio to parallel groups. The interventions will be performed by at least 10 physicians. All physicians will receive standardized training on device use, and procedures will be monitored by trained research staff. Data will be recorded in electronic Excel spreadsheets, and statistical analyses will be conducted using IBM SPSS Statistics. All ethical principles will be strictly observed, including obtaining informed consent from participants or their legal representatives. A Data and Safety Monitoring Committee will be established to oversee the study.

Conclusions: If the expected results are confirmed, patients, healthcare professionals, and healthcare institutions may benefit from the improved performance, safety, and usability of the experimental device, particularly in critical care settings and public health emergencies.

Conditions

• Orotracheal Intubation

Interventions

DEVICE

Orotracheal intubation using a 3D videolaryngoscope integrated with artificial intelligence (VL-IA).

The videolaryngoscope (VL) incorporates a microcamera at the tip of the blade, enabling real-time visualization of anatomical structures via a monitor. The VL to be used in the intervention was manufactured through additive manufacturing using polylactic acid (PLA) and polyethylene terephthalate glycol (PETG). It was validated in high-fidelity simulations for mechanical strength and usability and is patented under number BR 10 2020 026194 with the Brazilian National Institute of Industrial Property (INPI). The VL is currently being enhanced through the integration of artificial intelligence (AI) using computer vision techniques based on Machine Learning and Deep Learning, including convolutional neural networks capable of automatically recognizing upper airway anatomical structures from previously trained image datasets. The embedded system will provide real-time visual and audio guidance during intubation with less than one-second latency.

DEVICE

Commercially available gold-standard videolaryngoscope (VL-C)

Scientific evidence recommends the use of VL in all cases of intubation, across various clinical scenarios, as the primary intubation technique in clinical practice. The videolaryngoscope incorporates a microcamera at the tip of the blade, allowing visualization of anatomical structures in real time via a monitor.

Sponsors & Collaborators

• Universidade Federal de Santa Catarina

  collaborator OTHER

• Universidade Católica de Pelotas

  collaborator OTHER

• Universidade Federal do Rio Grande (FURG)

  collaborator OTHER

• Federal University of Pelotas

  lead OTHER

Principal Investigators

• Camila X Dalcól, PhD · University Federal of Santa Catarina

• Ines M Hirdes, Specialist · University Catolic of Pelotas

• Larissa O Daneluz, Master's degree · University Federal of Santa Catarina

• Andressa S Barboza, PhD · University Federal of Santa Catarina

• Marcelo C Ribeiro, Master's degree · University federal of Pelotas

• Tiago T Primo, PhD · University Federal of Pelotas

Study Design

Allocation

RANDOMIZED

Purpose

OTHER

Masking

SINGLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2027-03-20

Primary Completion

2027-12-31

Completion

2028-06-30

Countries

• Brazil

Study Locations