Clinical trial Recruiting at 1 site Observational Ages 15 to 70

Predicting difficult intubation from voice patterns before surgery

Official title: Research on a Machine Learning-Based Predictive Model for Difficult Intubation Using Specific Vocal Characteristics

Run by Shanghai Jiao Tong University Affiliated Sixth People's Hospital · NCT07278232 · Data updated 2026-11-12

This study tests whether acoustic features from specific vowel sounds can predict a difficult airway before anesthesia in people awaiting elective surgery.

Quick facts

Study typeObservational
Ages15 Years to 70 Years
SexAll
Enrollment300 (estimated)
SponsorShanghai Jiao Tong University Affiliated Sixth People's Hospital
Academic / other
Drugs / interventionschemotherapy
Locations1 site (Shanghai)
StatusRecruiting
StartedDecember 20, 2025
Main results expectedDecember 31, 2026
Study endsDecember 31, 2026
Registry record updatedAugust 24, 2026
Trial IDNCT07278232 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Adults and adolescents aged 15–70 scheduled for elective surgery requiring general anesthesia with tracheal intubation, who can complete voice recordings.

Probably not a fit

Patients with speech, hearing, or craniofacial disorders that affect voice recordings, or those unable to complete recordings, may not benefit from this approach.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This is a prospective, single-center, observational study enrolling up to 300 patients aged 15–70 who are scheduled for elective general anesthesia with tracheal intubation. Researchers will analyze voice recordings of selected vowels to identify patterns associated with difficult airway anatomy, using machine learning, while standard care and anesthesia procedures remain unchanged. The goal is to create a quick, non-invasive preoperative risk tool to help anesthesiologists anticipate challenging intubations and improve patient safety. The study excludes individuals with speech, hearing, or craniofacial conditions that could confound voice measurements or airway assessment. No interventions are being tested; data collection is observational and non-interventional.

Why it matters

Potential benefit: If successful, the tool could enable earlier planning and preparedness for difficult intubations, potentially reducing airway-related complications.

How similar studies have gone: Prior work using acoustic features and machine learning in airway predictions has shown promise in some settings but remains exploratory and not yet standard practice.

Who can join: the full rules

Show full inclusion / exclusion criteria
Inclusion Criteria:

* Patients scheduled for elective general anesthesia with tracheal intubation.

Exclusion Criteria:

* Patients with speech and pronunciation disorders (vocal cord diseases, cleft palate, craniofacial deformities, extensive tooth defects, cleft lip).
* Hearing impairment (difficulty in communication, inaccurate repetition).
* Patients with central nervous system diseases causing significant pronunciation abnormalities.
* Patients contraindicated for direct laryngoscopy (e.g., post-chemotherapy in the pharynx, diseases prone to mucosal bleeding, etc.) that affect group allocation, as well as those unable to complete the recording or temporarily reassigned to other anesthesia methods.

Where it's running

  • Shanghai Sixth People's Hospital Affiliated with Shanghai Jiao Tong University School of Medicine
    Shanghai, China
    Recruiting

Study contacts

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.