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NCT06215391UNKNOWNanonymous

Customized Masks in Non-Invasive Mechanical Ventilation: An Innovative Approach Through 3D Printing

Sponsor

Source record

Hospital Universitario 12 de Octubre

Phase

Source record

NA

Modality

AI-normalized

medical device

Target

AI-normalized

3d printed mask

Indication / condition

AI-normalized

COPD, Severe Early-Onset

Intervention

Source record

3d printed mask

Source & freshness

Source record

NCT ID

NCT06215391

Original source

ClinicalTrials.gov

Source last updated

Jan 22, 2024

Ingested at

Jun 16, 2026

Internal sync

Jun 16, 2026

Model version

trialsignal-ai-v1

Normalized confidence

96%

Validation status

validated

Open original registry record
View original source fields

NCT ID

NCT06215391

Title

Customized Masks in Non-Invasive Mechanical Ventilation: An Innovative Approach Through 3D Printing

Sponsor

Hospital Universitario 12 de Octubre

Status

UNKNOWN

Phase

NA

Condition raw

COPD, Severe Early-Onset, Ventilatory Failure, Als, Non Invasive Ventilation

Condition normalized

COPD, Severe Early-Onset, Ventilatory Failure, Als, Non Invasive Ventilation

Modality raw

medical device

Modality normalized

medical device

Target raw

3d printed mask

Target normalized

3d printed mask

Interventions

3d printed mask

Public preview

Source record

In non-invasive mechanical ventilation (NIMV), the interface is the primary determinant of success, as adherence and quality of therapy mainly depend on it. The aim of this study is to investigate the usefulness of a customised mask approach to minimise leakage and upper airway obstruction. It will focus on ventilator registries and changes in the way they can be corrected with these customised masks.

The process involves 3D face scanning and dedicated computer-aided design. The processing and manufacturing of the masks is based on additive manufacturing through 3D printing.

AI-generated analysis supports research triage only. Verify source records, publications, sponsor disclosures and IP databases before making diligence decisions. Model: trialsignal-ai-v1.

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