{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:13:03Z","timestamp":1790313183585,"version":"4.1.0"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T00:00:00Z","timestamp":1708992000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T00:00:00Z","timestamp":1708992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Pulm Med"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Pulmonary arterial hypertension is a serious medical condition. However, the condition is often misdiagnosed or a rather long delay occurs from symptom onset to diagnosis, associated with decreased 5-year survival. In this study, we developed and tested a deep-learning algorithm to detect pulmonary arterial hypertension using chest X-ray (CXR) images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>From the image archive of Chiba University Hospital, 259 CXR images from 145 patients with pulmonary arterial hypertension and 260 CXR images from 260 control patients were identified; of which 418 were used for training and 101 were used for testing. Using the testing dataset for each image, the algorithm outputted a numerical value from 0 to 1 (the probability of the pulmonary arterial hypertension score). The training process employed a binary cross-entropy loss function with stochastic gradient descent optimization (learning rate parameter, \u03b1\u2009=\u20090.01). In addition, using the same testing dataset, the algorithm\u2019s ability to identify pulmonary arterial hypertension was compared with that of experienced doctors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The area under the curve (AUC) of the receiver operating characteristic curve for the detection ability of the algorithm was 0.988. Using an AUC threshold of 0.69, the sensitivity and specificity of the algorithm were 0.933 and 0.982, respectively. The AUC of the algorithm\u2019s detection ability was superior to that of the doctors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The CXR image-derived deep-learning algorithm had superior pulmonary arterial hypertension detection capability compared with that of experienced doctors.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12890-024-02891-4","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T07:03:01Z","timestamp":1709017381000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Artificial intelligence-based model for predicting pulmonary arterial hypertension on chest x-ray images"],"prefix":"10.1186","volume":"24","author":[{"given":"Shun","family":"Imai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seiichiro","family":"Sakao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Nagata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akira","family":"Naito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayumi","family":"Sekine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshihiko","family":"Sugiura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayako","family":"Shigeta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akira","family":"Nishiyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hajime","family":"Yokota","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Norihiro","family":"Shimizu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeshi","family":"Sugawara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshiaki","family":"Nomi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seiwa","family":"Honda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keisuke","family":"Ogaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nobuhiro","family":"Tanabe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takayuki","family":"Baba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takuji","family":"Suzuki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"key":"2891_CR1","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1378\/chest.09-1140","volume":"137","author":"DB Badesch","year":"2010","unstructured":"Badesch DB, Raskob GE, Elliott CG, Krichman AM, Farber HW, Frost AE, et al. Pulmonary arterial hypertension: baseline characteristics from the REVEAL Registry. Chest. 2010;137:376\u201387.","journal-title":"Chest"},{"key":"2891_CR2","doi-asserted-by":"publisher","first-page":"1801904","DOI":"10.1183\/13993003.01904-2018","volume":"53","author":"A Frost","year":"2019","unstructured":"Frost A, Badesch D, Gibbs JSR, Gopalan D, Khanna D, Manes A, et al. Diagnosis of pulmonary hypertension. Eur Respir J. 2019;53:1801904.","journal-title":"Eur Respir J"},{"key":"2891_CR3","doi-asserted-by":"publisher","first-page":"3618","DOI":"10.1093\/eurheartj\/ehac237","volume":"43","author":"M Humbert","year":"2022","unstructured":"Humbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, et al. 2022 ESC\/ERS guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J. 2022;43:3618\u2013731.","journal-title":"Eur Heart J"},{"key":"2891_CR4","doi-asserted-by":"publisher","first-page":"1801889","DOI":"10.1183\/13993003.01889-2018","volume":"53","author":"N Gali\u00e8","year":"2019","unstructured":"Gali\u00e8 N, Channick RN, Frantz RP, Gr\u00fcnig E, Jing ZC, Moiseeva O, et al. Risk stratification and medical therapy of pulmonary arterial hypertension. Eur Respir J. 2019;53:1801889.","journal-title":"Eur Respir J"},{"key":"2891_CR5","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1186\/s12890-022-02073-0","volume":"22","author":"J Nagata","year":"2022","unstructured":"Nagata J, Sekine A, Tanabe N, Taniguchi Y, Ishida K, Shiko Y, et al. Mixed venous oxygen tension is a crucial prognostic factor in pulmonary hypertension: a retrospective cohort study. BMC Pulm Med. 2022;22:282.","journal-title":"BMC Pulm Med"},{"key":"2891_CR6","doi-asserted-by":"publisher","first-page":"89","DOI":"10.4103\/2045-8932.109919","volume":"3","author":"G Strange","year":"2013","unstructured":"Strange G, Gabbay E, Kermeen F, Williams T, Carrington M, Stewart S, et al. Time from symptoms to definitive diagnosis of idiopathic pulmonary arterial hypertension: the delay study. Pulm Circ. 2013;3:89\u201394.","journal-title":"Pulm Circ"},{"key":"2891_CR7","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1111\/resp.13768","volume":"25","author":"V Khou","year":"2020","unstructured":"Khou V, Anderson JJ, Strange G, Corrigan C, Collins N, Celermajer DS, et al. Diagnostic delay in pulmonary arterial hypertension: insights from the Australian and New Zealand pulmonary hypertension registry. Respirology. 2020;25:863\u201371.","journal-title":"Respirology"},{"key":"2891_CR8","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1111\/resp.13814","volume":"25","author":"J Weatherald","year":"2020","unstructured":"Weatherald J, Humbert M. The \u2018great wait\u2019 for diagnosis in pulmonary arterial hypertension. Respirology. 2020;25:790\u20132.","journal-title":"Respirology"},{"key":"2891_CR9","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1186\/s12938-018-0544-y","volume":"17","author":"C Qin","year":"2018","unstructured":"Qin C, Yao D, Shi Y, Song Z. Computer-aided detection in chest radiography based on artificial intelligence: a survey. Biomed Eng OnLine. 2018;17:113.","journal-title":"Biomed Eng OnLine"},{"key":"2891_CR10","first-page":"423","volume":"19","author":"NR Parveen","year":"2011","unstructured":"Parveen NR, Sathik MM. Detection of Pneumonia in chest X-ray images. J Xray Sci Technol. 2011;19:423\u20138.","journal-title":"J Xray Sci Technol"},{"key":"2891_CR11","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.media.2005.09.003","volume":"10","author":"AM Schilham","year":"2006","unstructured":"Schilham AM, van Ginneken B, Loog M. A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database. Med Image Anal. 2006;10:247\u201358.","journal-title":"Med Image Anal"},{"key":"2891_CR12","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1148\/radiol.2017162326","volume":"284","author":"P Lakhani","year":"2017","unstructured":"Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284:574\u201382.","journal-title":"Radiology"},{"key":"2891_CR13","doi-asserted-by":"publisher","first-page":"2102269","DOI":"10.1183\/13993003.02269-2021","volume":"61","author":"H Nishikiori","year":"2023","unstructured":"Nishikiori H, Kuronuma K, Hirota K, Yama N, Suzuki T, Onodera M, et al. Deep learning algorithm to detect fibrosing interstitial lung disease on chest radiographs. Eur Respir J. 2023;61:2102269.","journal-title":"Eur Respir J"},{"key":"2891_CR14","doi-asserted-by":"publisher","first-page":"19311","DOI":"10.1038\/s41598-020-76359-w","volume":"10","author":"K Kusunose","year":"2020","unstructured":"Kusunose K, Hirata Y, Tsuji T, Kotoku J, Sata M. Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray. Sci Rep. 2020;10:19311.","journal-title":"Sci Rep"},{"key":"2891_CR15","doi-asserted-by":"publisher","first-page":"1801897","DOI":"10.1183\/13993003.01897-2018","volume":"53","author":"JL Vachi\u00e9ry","year":"2019","unstructured":"Vachi\u00e9ry JL, Tedford RJ, Rosenkranz S, Palazzini M, Lang I, Guazzi M, et al. Pulmonary hypertension due to left heart disease. Eur Respir J. 2019;53:1801897.","journal-title":"Eur Respir J"},{"key":"2891_CR16","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. p. 770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"key":"2891_CR17","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, PyTorch: An imperative style, high-performance deep learning library. In: Adv Neural Inf Process Syst 32: Annual Conference on Neural Information Processing Systems, NeurIPS 2019. Vancouver, BC, Canada, 2019. Wallach HM, Larochelle H, Beygelzimer A et al. d\u2019Alch\u00e9-Buc F, Fox EA, Garnett R, editors; 2019."}],"container-title":["BMC Pulmonary Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12890-024-02891-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12890-024-02891-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12890-024-02891-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T07:03:05Z","timestamp":1709017385000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcpulmmed.biomedcentral.com\/articles\/10.1186\/s12890-024-02891-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,27]]},"references-count":17,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2891"],"URL":"https:\/\/doi.org\/10.1186\/s12890-024-02891-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3214956\/v1","asserted-by":"object"}]},"ISSN":["1471-2466"],"issn-type":[{"value":"1471-2466","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,27]]},"assertion":[{"value":"29 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was performed in accordance with the Declaration of Helsinki. This study was approved by Research Ethics committee of the Graduate School of Medicine, Chiba University (approval number: 4203). All adult participants provided written informed consent to participate.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"TN, SH, and KO are employees of M3 Inc. All other authors do not have competing interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"101"}}