biostudies-literatureUnknown6(2)Kishikawa R<h4>Aims</h4>Delayed diagnosis of pulmonary hypertension (PH) is a known cause of poor patient prognosis. We aimed to develop an artificial intelligence (AI) model, using ensemble learning method to detect PH using electrocardiography (ECG), chest X-ray (CXR), and brain natriuretic peptide (BNP), facilitating accurate detection and prompting further examinations.<h4>Methods and results</h4>We developed a convolutional neural network model using ECG data to predict PH, labelled by ECG from seven institutions. Logistic regression was used for the BNP prediction model. We referenced a CXR deep learning model using ResNet18. Outputs from each of the three models were integrated into a three-layer fully connected multimodal model. Ten cardiologists participated in an interpretation test, detectEuropean heart journal. Digital health209-217https://www.ebi.ac.uk/biostudies/studies/S-EPMC11914732biostudies-literatureAn ensemble learning model for detection of pulmonary hypertension using electrocardiogram, chest X-ray, and brain natriuretic peptide.PMC11914732Takeda NIshida JKishikawa RShinohara HSetoguchi NAkazawa HFujita HNanasato MAndo JKushida SMinatsuki SFujiu KKodera SMaki HSawano SNakanishi KTanabe KKato NWatanabe HSawada NSato MMorita HTakahashi MfalseAn ensemble learning model for detection of pulmonary hypertension using electrocardiogram, chest X-ray, and brain natriuretic peptide.<h4>Aims</h4>Delayed diagnosis of pulmonary hypertension (PH) is a known cause of poor patient prognosis. We aimed to develop an artificial intelligence (AI) model, using ensemble learning method to detect PH using electrocardiography (ECG), chest X-ray (CXR), and brain natriuretic peptide (BNP), facilitating accurate detection and prompting further examinations.<h4>Methods and results</h4>We developed a convolutional neural network model using ECG data to predict PH, labelled by ECG from seven institutions. Logistic regression was used for the BNP prediction model. We referenced a CXR deep learning model using ResNet18. Outputs from each of the three models were integrated into a three-layer fully connected multimodal model. Ten cardiologists participated in an interpretation test, detect2025-01-01T00:00:00Z2025 Mar2025-04-04T00:08:22.026Z2025-04-04T00:08:22.026ZS-EPMC119147324011021410.1093/ehjdh/ztae097