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Accurate diagnosis of pulmonary embolism using ventilation/perfusion scintigraphy and clinical assessment by artificial intelligence

Accurate diagnosis of pulmonary embolism using ventilation/perfusion scintigraphy and clinical assessment by artificial intelligence

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000030448
Enrollment
Unknown
Registered
2020-03-02
Start date
2021-01-01
Completion date
Unknown
Last updated
2020-03-09

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

Conditions

pulmonary embolism

Interventions

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Sponsors

Xinhua Hospital, Shanghai Jiaotong University School of Medicine
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Patients who meet the diagnostic criteria of pulmonary embolism guidelines

Exclusion criteria

Exclusion criteria: 1) No treatment for various reasons; 2) Refuse or fail to cooperate with follow-up treatment.

Design outcomes

Primary

MeasureTime frame
Ventilation/Perfusion Scintigraphy;CT pulmonary angiography;D-D;TNI;BNP;Pulmonary artery pressure;—

Secondary

MeasureTime frame
DLCO;DLCO;DLCO/VA;PO2;—

Countries

China

Contacts

Public ContactYe Wenjiing

Xinhua Hospital, Shanghai Jiaotong University School of Medicine

yewenjing0418@163.com+86 13671691558

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 19, 2026