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Diagnosis and efficacy prediction of dermoscopic images based on convolutional neural networks for vitiligo in childhood and related metabolomic analysis

Diagnosis and efficacy prediction of dermoscopic images based on convolutional neural networks for vitiligo in childhood and related metabolomic analysis

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200064339
Enrollment
Unknown
Registered
2022-10-03
Start date
2022-10-01
Completion date
Unknown
Last updated
2023-05-15

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

Conditions

vitiligo

Interventions

Case series:None

Sponsors

Children's Hospital of Chongqing Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
No minimum to 18 Years

Inclusion criteria

Inclusion criteria: 1. Aged from 0 to 18, male and female unlimited; 2. Patients diagnosed with vitiligo on the basis of history, clinical presentation, Wood's lamp, and dermoscopic signs. 3. To sign the informed consent.

Exclusion criteria

Exclusion criteria: 1. Patients currently undergoing phototherapy, systemic glucocorticoid therapy, immunosuppressive therapy; 2. Patients with severe infectious diseases, psychiatric disorders, primary or secondary immunodeficiencies, tumours; 3. Photosensitive diseases (SLE, dermatomyositis), etc.; 4. Guardians with psychiatric disorders, mental abnormalities, cognitive impairment, etc.; 5. Patients who are unable to follow up regularly with medical advice; 6. Patients who are allergic to glucocorticoids such as methylprednisolone, Eloson, etc.

Design outcomes

Primary

MeasureTime frame
Dermatoscopic features;—

Countries

China

Contacts

Public ContactYu Shijuan

Children's Hospital of Chongqing Medical University

375028722@qq.com+86 63638830

Outcome results

None listed

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