{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:35:38Z","timestamp":1790703338651,"version":"4.1.0"},"reference-count":26,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":34,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pulm. circ."],"published-print":{"date-parts":[[2023,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Echocardiography, a simple and noninvasive tool, is the first choice for screening pulmonary hypertension (PH). However, accurate assessment of PH, incorporating both the pulmonary artery pressures and additional signs for PH remained unsatisfied. Thus, this study aimed to develop a machine learning (ML) model that can automatically evaluate the probability of PH. This cohort included data from 346 (275 for training set and internal validation set and 71 for external validation set) patients with suspected PH patients and receiving right heart catheterization.\u00a0Echocardiographic images on parasternal short axis\u2010papillary muscle level (PSAX\u2010PML) view from all patients were collected, labeled, and preprocessed. Local features from each image were extracted and subsequently integrated to build a ML model. By adjusting the parameters of the model, the model with the best prediction effect is finally constructed. We used receiver\u2010operating characteristic analysis to evaluate model performance and compared the ML model with the traditional methods. The accuracy of the ML model for diagnosis of PH was significantly higher than the traditional method (0.945 vs. 0.892, <jats:italic>p<\/jats:italic>\u2009=\u20090.027 [area under the curve [AUC]]). Similar findings were observed in subgroup analysis and validated in the external validation set (AUC\u2009=\u20090.950 [95% CI:\u00a00.897\u22121.000]). In summary, ML\u00a0methods could automatically extract features from traditional PSAX\u2010PML view and automatically assess the probability of PH, which were found to outperform traditional echocardiographic assessments.<\/jats:p>","DOI":"10.1002\/pul2.12272","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T07:49:32Z","timestamp":1691135372000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Automatic echocardiographic evaluation of the probability of pulmonary hypertension using machine learning"],"prefix":"10.1002","volume":"13","author":[{"given":"Zuwei","family":"Liao","sequence":"first","affiliation":[{"name":"Shantou University Medical College Shantou Guangdong China"},{"name":"Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou Guangdong 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