Predicting the appropriate cuff volume for endotracheal tubes using machine learning

Predicting the appropriate cuff volume for endotracheal tubes using machine learning - Predicting the appropriate cuff volume for endotracheal tubes using machine learning

Status

Active, not recruiting

Phases

Unknown

Study type

Interventional

Source

JPRN

Registry ID

JPRN-UMIN000054629

Enrollment

250

Registered

2024-06-13

Start date

2024-06-12

Completion date

Unknown

Last updated

2026-09-14

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Patients who undergo surgery under general anesthesia with tracheal intubation

Interventions

use of endotracheal tubes during tracheal intubation procedure

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Patients aged 20 yr or older, with ASA physical status I-III, scheduled to receive general anesthesia and tracheal intubation

Exclusion criteria

Exclusion criteria: Patients with pharyngeal pathology, at risk of pulmonary aspiration of gastric contents, or predicted difficult mask ventilation

Design outcomes

Primary

Measure	Time frame
mean squared error	—

Countries

Japan

Contacts

Public ContactYuji Soeda

Kitakyushu General Hospital Department of Anesthesia

soecchi_y@yahoo.co.jp093-921-0560

Outcome results

None listed

Source: JPRN (via WHO ICTRP) · Data processed: Sep 19, 2026