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Automatic Detection of OSAHS From Facial Photographs Using Machine Learning Methods

Automatic Detection of OSAHS From Facial Photographs Using Machine Learning Methods

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200062476
Enrollment
Unknown
Registered
2022-08-08
Start date
2022-08-08
Completion date
Unknown
Last updated
2023-04-12

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

Conditions

Obstructive sleep apnea hypopnea syndrome,OSAHS

Interventions

Gold Standard:According to the diagnostic criteria proposed in my country's revised guideline for the diagnosis and treatment of obstructive sleep apnea hypopnea syndrome in 2011 . 1. Clinical symptom
Index test:AHI

Sponsors

Zhongshan Hospital Affiliated to Fudan University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1. Aged between 18 and 80 years old (including both boundary values); 2. Have performed or will perform sleep monitoring (polysomnography PSG or portable primary screening PM) in Zhongshan Hospital Affiliated to Fudan University; 3. Voluntarily participate in this study and sign informed consent.

Exclusion criteria

Exclusion criteria: 1. Subjects who have large-area burns, rashes, scars, ear and nose defects, etc. on the face, which affect the AI analysis of portraits.

Design outcomes

Primary

MeasureTime frame
Apnea-Hypopnea Index, AHI;—

Countries

China

Contacts

Public ContactYuanlin Song

Zhongshan Hospital Affiliated to Fudan University

song.yuanlin@zs-hospital.sh.cn+86 15021757762

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026