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Artificial Intelligent Image Processing and Diagnosis of Pulmonary Vessels in CT

NCT06589843 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 15000

Last updated 2024-09-19

No results posted yet for this study

Summary

In this study, patients with chest pain, lung cancer, pulmonary embolism, and routine inpatient physical examination were selected as the research objects, and the experimental design of retrospective cohort study was adopted to carry out artificial intelligence analysis related to pulmonary vascular diseases in patients with multi-dimensional big data. The multi-modal CT acquisition process included plain scan CT(NCCT) and CT pulmonary angiography (CTPA). Ctpa-like image effects can be simulated or reconstructed by non-enhanced plain scan CT images, so that CTPA-like image quality can be obtained without injecting contrast agent. The synthetic CTPA images were further analyzed by artificial intelligence to assist doctors in the intelligent diagnosis of pulmonary vascular diseases.

Conditions

  • Radiology
  • Vascular Diseases

Interventions

DIAGNOSTIC_TEST

Deep learning imaging enhancement

Conventional imaging or down-sampling imaging from CT or MR are enhanced by approved deep learning method.

Sponsors & Collaborators

  • Xin Lou

    lead OTHER

Principal Investigators

  • Xin Lou · Chinese PLA General Hospital

Eligibility

Min Age
18 Years
Max Age
100 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-09-10
Primary Completion
2029-09-01
Completion
2029-09-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06589843 on ClinicalTrials.gov