Artificial Intelligent Image Processing and Diagnosis of Pulmonary Vessels in CT
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-100
SponsorXin Lou
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Age ≥18≤100 years old Scan the pulmonary artery and its major branches Patients with suspected pulmonary embolism who received CTPA had a set of CTPA and CT scans The image quality meets the requirements of diagnosis and post-processing Patients who completed the examination in accordance with the data collection criteria Clinical data and follow-up were complete
Disqualifiers
Age <18 years or age >100 years The image is incomplete or incorrect Pulmonary artery absent or underenhanced Severe motion artifacts or image noise affect evaluation of pulmonary embolism History of aortic reconstruction, replacement, or stent implantation Congenital variations in the whole or important branches of the aorta in adults (e.g. bovine aortic arch, abnormal right subclavian artery) Severe hypovolemia and hemodynamic instability Severe heart failure with low ejection fraction Dialysis patient
Trial population
Patients with chest pain, lung cancer, pulmonary embolism, and routine inpatient physical examination were studied
Trial design
Design model
Case-only
Time perspective
Prospective
Treatments tested in this trial
Deep learning imaging enhancement
Diagnostic testConventional imaging or down-sampling imaging from CT or MR are enhanced by approved deep learning method.
Treatment groups
No treatment groups listed
Trial outcomes
Primary outcomes
1
The performance of deep enhanced imaging in lesion detection and diagnosis
The performance of deep enhanced imaging in lesion detection and diagnosis, including imaging quality, accuracy, sensitivity and specificity in lesion detection and imaging diagnosis.
Time frame
2 yearLocations
Sponsors and contacts
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Xin Lou
Lead sponsor
Chinese PLA General Hospital
Sponsor institution
Source ClinicalTrials.gov
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