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Clinical Study on an Artificial Intelligence-Assisted Chest Radiograph Model Based on Big Data and Deep Learning for Early Detection of Kawasaki Disease

Clinical Study on an Artificial Intelligence-Assisted Chest Radiograph Model Based on Big Data and Deep Learning for Early Detection of Kawasaki Disease

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500114634
Enrollment
Unknown
Registered
2025-12-16
Start date
2025-12-17
Completion date
Unknown
Last updated
2026-01-05

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

Conditions

Mucocutaneous lymph node syndrome

Interventions

Control Group:None
Case Group:None

Sponsors

Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Lead Sponsor

Eligibility

Sex/Gender
All
Age
No minimum to 18 Years

Inclusion criteria

Inclusion criteria: 1.Case group: (1) The age of seeking medical treatment is less than or equal to 18 years old; (2) The medical record system diagnosis contains the diagnosis of "Kawasaki Disease", "mucocutaneous lymph node syndrome" or "IVIG non-response Kawasaki disease". (3) At least one complete chest X-ray examination data (images and reports) is available during the same hospitalization. 2. Control group: (1) The age of seeking medical treatment is less than or equal to 18 years old; (2) The same period as the case group; (3) Fever lasts for 3 days or more; (4) Rule out the possibility of diagnosing Kawasaki disease

Exclusion criteria

Exclusion criteria: 1.Case group: (1) Chest X-ray quality issues: Severe artifacts, overexposure/underexposure leading to inability to assess key structures. (2) Incomplete clinical information, including lack of chest X-ray examination, laboratory tests, and unclear days of fever. (3) Inability to determine the final diagnosis (such as loss to follow-up, diagnosis in doubt). 2. Control group: (1) Chest X-ray quality issues: Severe artifacts, overexposure/underexposure leading to inability to assess key structures. (2) Incomplete clinical information, including lack of chest X-ray examination, laboratory tests, and unclear days of fever. (3) Inability to make a clear final diagnosis (such as loss to follow-up, questionable diagnosis).

Design outcomes

Primary

MeasureTime frame
Sensitivity;Area Under Curve;Specificity;

Countries

China

Contacts

Public ContactJian Wang

Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine

wangjian@xinhuamed.com.cn+86 21 2507 8485

Outcome results

None listed

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