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Artificial Intelligence-assisted Evaluation of Pulmonary HYpertension

Artificial Intelligence-Assisted Evaluation of Pulmonary Hypertension

Important: This information is not medical advice. Talk to your doctor about whether a clinical trial is right for you.

About This Trial

Pulmonary hypertension represents a challenging and heterogeneous condition that is associated with high mortality and morbidity if left untreated. Artificial intelligence is used to study and develop theories and methods that simulate and extend human intelligence, which is being applied in fields related to cardiovascular diseases. The study intends to combine multimodal clinical data of patients who undergo right heart catheterization at Fuwai Hospital with artificial intelligence techniques to create programs that can screen and diagnose pulmonary hypertension.

Who May Be Eligible (Plain English)

Who May Qualify: - Age ≥18 years old - Patients previously received chest X-ray, electrocardiography, echocardiography, other routine examinations, and RHC at the Fuwai Hospital, CAMS \& PUMC, Beijing, China Who Should NOT Join This Trial: - Patients without RHC - The quality of routine examinations and RHC cannot meet the requirement for further analysis - Severe loss of results of routine examinations (chest X-ray, electrocardiography, echocardiography, etc.) Always talk to your doctor about whether this trial is right for you.

Original Eligibility Criteria

View original clinical language
Inclusion Criteria: * Age ≥18 years old * Patients previously received chest X-ray, electrocardiography, echocardiography, other routine examinations, and RHC at the Fuwai Hospital, CAMS \& PUMC, Beijing, China Exclusion Criteria: * Patients without RHC * The quality of routine examinations and RHC cannot meet the requirement for further analysis * Severe loss of results of routine examinations (chest X-ray, electrocardiography, echocardiography, etc.)

Treatments Being Tested

DIAGNOSTIC_TEST

Right heart catheterization

RHC is commonly used essential test to make gold-standard diagnosis of PH with mPAP \>20 mmHg. All multimodal data from patients eligible for inclusion would be randomly assigned to development datasets (70% of the study population) to train the artificial intelligence models for the detection of PH, which would be validated and tested by other datasets (30% of the study population).

Locations (1)

Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Beijing, Beijing Municipality, China