{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T02:17:49Z","timestamp":1782181069600,"version":"3.54.5"},"reference-count":44,"publisher":"SAGE Publications","issue":"12","license":[{"start":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T00:00:00Z","timestamp":1605571200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Acta Radiol"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cardiomegaly is a relatively common incidental finding on chest X-rays; if left untreated, it can result in significant complications. Using Artificial Intelligence for diagnosing cardiomegaly could be beneficial, as this pathology may be underreported, or overlooked, especially in busy or under-staffed settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To explore the feasibility of applying four different transfer learning methods to identify the presence of cardiomegaly in chest X-rays and to compare their diagnostic performance using the radiologists\u2019 report as the gold standard.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods<\/jats:title>\n                    <jats:p>Two thousand chest X-rays were utilized in the current study: 1000 were normal and 1000 had confirmed cardiomegaly. Of these exams, 80% were used for training and 20% as a holdout test dataset. A total of 2048 deep features were extracted using Google\u2019s Inception V3, VGG16, VGG19, and SqueezeNet networks. A logistic regression algorithm optimized in regularization terms was used to classify chest X-rays into those with presence or absence of cardiomegaly.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Diagnostic accuracy is reported by means of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), with the VGG19 network providing the best values of sensitivity (84%), specificity (83%), PPV (83%), NPV (84%), and overall accuracy (84,5%). The other networks presented sensitivity at 64.1%\u201382%, specificity at 77.1%\u201381.1%, PPV at 74%\u201381.4%, NPV at 68%\u201382%, and overall accuracy at 71%\u201381.3%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Deep learning using transfer learning methods based on VGG19 network can be used for the automatic detection of cardiomegaly on chest X-ray images. However, further validation and training of each method is required before application to clinical cases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/0284185120973630","type":"journal-article","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T00:10:26Z","timestamp":1605658226000},"page":"1601-1609","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":20,"title":["Identifying cardiomegaly in chest X-rays: a cross-sectional study of evaluation and comparison between different transfer learning methods"],"prefix":"10.1177","volume":"62","author":[{"given":"Haralabos","family":"Bougias","sequence":"first","affiliation":[{"name":"Department of Clinical Radiology, Ioannina University Hospital, Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleni","family":"Georgiadou","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Metaxa Anticancer Hospital, Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Malamateniou","sequence":"additional","affiliation":[{"name":"Division of Midwifery and Radiography, School of Health Sciences, City University of London, London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1378-6631","authenticated-orcid":false,"given":"Nikolaos","family":"Stogiannos","sequence":"additional","affiliation":[{"name":"Division of Midwifery and Radiography, School of Health Sciences, City University of London, London, UK"},{"name":"Department of Medical Imaging, Corfu General Hospital, Corfu, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2020,11,17]]},"reference":[{"key":"bibr1-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1080\/16878507.2020.1756187"},{"key":"bibr2-0284185120973630","volume-title":"Taylor\u2019s differential diagnosis manual: symptoms and signs in the time-limited encounter","author":"Paulman PM","year":"2013"},{"key":"bibr3-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCRESAHA.117.309711"},{"key":"bibr4-0284185120973630","volume":"27","author":"Brakohiapa EKK","year":"2017","journal-title":"Pan Afr Med J"},{"key":"bibr5-0284185120973630","doi-asserted-by":"publisher","DOI":"10.4314\/gmj.v49i3.6"},{"key":"bibr6-0284185120973630","doi-asserted-by":"publisher","DOI":"10.5812\/ircmj.18242"},{"key":"bibr7-0284185120973630","doi-asserted-by":"publisher","DOI":"10.7861\/futurehosp.6-2-94"},{"key":"bibr8-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-019-0681-4"},{"key":"bibr9-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1016\/j.gpb.2017.07.003"},{"key":"bibr10-0284185120973630","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8030292"},{"key":"bibr11-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1177\/1533033819858363"},{"key":"bibr12-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2017171115"},{"key":"bibr13-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/3640901"},{"key":"bibr14-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.010"},{"key":"bibr15-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13221"},{"key":"bibr16-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"bibr17-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528162"},{"key":"bibr18-0284185120973630","doi-asserted-by":"publisher","DOI":"10.3390\/s19112645"},{"key":"bibr19-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"bibr20-0284185120973630","doi-asserted-by":"publisher","DOI":"10.1007\/s13244-018-0639-9"},{"key":"bibr21-0284185120973630","volume-title":"The research process in nursing","author":"Gerrish K","year":"2015","edition":"7"},{"key":"bibr22-0284185120973630","doi-asserted-by":"crossref","unstructured":"Yim J, Joo D, Bae J, et\u00a0al. 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