Background and Aims: Screening for hepatopulmonary syndrome in cirrhotic patients is limited due to the need to perform contrast enhanced echocardiography (CEE) and arterial blood gas (ABG) analysis. We aimed to develop a simple and quick method to screen for the presence of in-trapulmonary vascular dilation (IPVD) using noninvasive and easily available variables with machine learning (ML) algorithms. Methods: Cirrhotic patients were enrolled from our hospital. All eligible patients underwent CEE, ABG analysis and physical examination. We developed a two-step model based on three ML algorithms, namely, adap-tive boosting (termed AdaBoost), gradient boosting deci-sion tree (termed GBDT) and eXtreme gradient boosting (termed Xgboost). Noninvasive variables were input in the first step (the NI model), and for the second step (the NIBG model), a combination of noninvasive variables and ABG re-sults were used. Model performance was determined by the area under the curve of receiver operating characteristics (AUCROCs), precision, recall, F1-score and accuracy. Re-sults: A total of 193 cirrhotic patients were ultimately ana-lyzed. The AUCROCs of the NI and NIBG models were 0.850 (0.738-0.962) and 0.867 (0.760-0.973), respectively, and both had an accuracy of 87.2％. For both negative and posi-tive cases, the recall values of the NI and NIBG models were both 0.867 (0.760-0.973) and 0.875 (0.771-0.979), respectively, and the precisions were 0.813 (0.690-0.935) and 0.913 (0.825-1.000), respectively. Conclusions: We developed a two-step model based on ML using noninvasive variables and ABG results to screen for the presence of IPVD in cirrhotic patients. This model may partly solve the prob-lem of limited access to CEE and ABG by a large numbers of cirrhotic patients.