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Predicting pediatric OSA through deep learning model of upper airway fluid mechanics

Construction of a risk prediction model for obstructive sleep apnea in children based on deep learning of upper airway fluid mechanics

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600127393
Enrollment
Unknown
Registered
2026-06-30
Start date
2026-07-01
Completion date
Unknown
Last updated
2026-07-13

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

Conditions

Children OSA

Interventions

Moderate OSA group:None
Mild OSA group:None
Severe OSA group:None
Control group:None

Sponsors

Shanghai Stomatological Hospital, Fudan University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
7 Years to 18 Years

Inclusion criteria

Inclusion criteria: 1. Age range: 7 - 18 years old; 2. Had a complete overnight PSG examination and obtained standardized sleep breathing parameters; 3. Taking high-quality CBCT images of the head and neck at the same time as the PSG examination.

Exclusion criteria

Exclusion criteria: 1. Children with severe craniofacial deformities, such as cleft lip and palate, trisomy syndrome, etc. 2. Having neurological or muscular disorders or other systemic diseases that affect respiratory function; 3. Missing or incomplete CBCT or PSG data.

Design outcomes

Primary

MeasureTime frame
Maximum airflow velocity of the upper airway;—

Secondary

MeasureTime frame
accuracy;sensitivity;specificity;—

Countries

China

Contacts

Public ContactGang Yang

Shanghai Stomatological Hospital, Fudan University

641306366@qq.com+86 191 1712 8661

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Jul 23, 2026