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Research on enhancing the diagnostic efficiency of radionuclide lung imaging in CTEPH based on artificial intelligence

Research on enhancing the diagnostic efficiency of radionuclide lung imaging in CTEPH based on artificial intelligence

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500110092
Enrollment
Unknown
Registered
2025-09-30
Start date
2025-03-06
Completion date
Unknown
Last updated
2025-10-06

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

Conditions

Chronic thromboembolic pulmonary hypertension

Interventions

Index test:AI diagnostic model construction using pulmonary ventilation/perfusion imaging and FAPI PET/CT imaging

Sponsors

Beijing Chaoyang Hospital, Capital Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Age >=18 years old; 2. V/Q SPECT imaging and right heart catheterization were completed and the clinical diagnosis was CTEPH or excluded from the diagnosis of CTEPH. After 3 months of standardized anticoagulant therapy, imaging confirmed the presence of chronic thrombosis. b. Mean pulmonary artery pressure (mPAP>=25mmHg) and pulmonary artery wedge pressure (PAWP=25mmHg) and pulmonary artery wedge pressure (PAWP=25mmHg) and pulmonary capillary pressure (PCWP < 15mmHg) at rest; b. All diseases known to cause increased pulmonary arterial pressure were excluded. 2. No history of myocardial infarction and severe coronary atherosclerotic disease. 3. No serious comorbidities or clinical instability. 4. CTEPH patients signed informed consent and agreed to be followed up for 2 years after enrollment.

Exclusion criteria

Exclusion criteria: 1. Age < 18 years old; 2. Poor image quality, which cannot meet the diagnostic requirements of artificial intelligence; 3. No definite clinical diagnosis was obtained.

Design outcomes

Primary

MeasureTime frame
The sensitivity, specificity and AUC of the AI model;The SSIM and PSNR of the predicted images generated by the AI image generation model and the actual imaging results after treatment were compared;Pre-treatment and post-treatment perfusion difference maps generated;Al18F-NOTA-FAPI-04 imaging;

Countries

China

Contacts

Public ContactLi Wang

Beijing Chaoyang Hospital, Capital Medical University

lanyou925@126.com+86 10 8523 1356

Outcome results

None listed

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