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Identifying Orbital Diseases Using Deep Learning Based on Multimodal Data: A Multicenter, Retrospective Clinical Study

Identifying Orbital Diseases Using Deep Learning Based on Multimodal Data: A Multicenter, Retrospective Clinical Study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400081033
Enrollment
Unknown
Registered
2024-02-20
Start date
2023-01-01
Completion date
Unknown
Last updated
2024-02-26

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

Conditions

Orbital disease

Interventions

Orbital disease patients:Facial images, demographic information, diagnostic information, imaging information
Healthy people:Facial images, demographic information, diagnostic information, imaging information

Sponsors

Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: (1) Patients with orbital diseases: 1. Chinese, 18 years old and above, regardless of gender; 2. Meets the diagnostic criteria for orbital diseases; 3. Having clear facial data: It is required to have no obvious scars or birthmarks on the orbit and face, no facial obstruction by other objects, no makeup, no mask, and a complete forehead and facial angle. (2) Healthy people: Has clear facial data: Requires no obvious scars or birthmarks on the orbit and face, no facial obstruction by other objects, no makeup, no mask, and a complete forehead and facial angle.

Exclusion criteria

Exclusion criteria: (1) Patients with orbital diseases: None. (2) Healthy people: There are abnormal signs on the surface of the eye, eyelids, and orbit (such as exophthalmos, enophthalmos, eyelid retraction, ptosis, entropion, ectropion, abnormal position of the inner and outer canthus, and high differences in orbital symmetry).

Design outcomes

Primary

MeasureTime frame
diagnosis of orbital disease;

Countries

China

Contacts

Public ContactHuifang Zhou

Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine

fangzzfang@sjtu.edu.cn+86 136 6190 1886

Outcome results

None listed

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