A deep learning-based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation
DC Field | Value | Language |
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dc.contributor.author | Kim, Cherry | - |
dc.contributor.author | Lee, Gaeun | - |
dc.contributor.author | Oh, Hongmin | - |
dc.contributor.author | Jeong, Gyujun | - |
dc.contributor.author | Kim, Sun Won | - |
dc.contributor.author | Chun, Eun Ju | - |
dc.contributor.author | Kim, Young-Hak | - |
dc.contributor.author | Lee, June-Goo | - |
dc.contributor.author | Yang, Dong Hyun | - |
dc.date.accessioned | 2022-03-03T11:40:36Z | - |
dc.date.available | 2022-03-03T11:40:36Z | - |
dc.date.created | 2022-02-08 | - |
dc.date.issued | 2022-03 | - |
dc.identifier.issn | 0938-7994 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/137616 | - |
dc.description.abstract | Objectives Cardiovascular border (CB) analysis is the primary method for detecting and quantifying the severity of cardiovascular disease using posterior-anterior chest radiographs (CXRs). This study aimed to develop and validate a deep learning-based automatic CXR CB analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD). Methods We developed CB_auto using 816 normal and 798 VHD CXRs. For validation, 640 normal and 542 VHD CXRs from three different hospitals and 132 CXRs from a public dataset were assigned. The reliability of the CB parameters determined by CB_auto was evaluated. To evaluate the differences between parameters determined by CB_auto and manual CB drawing (CB_hand), the absolute percentage measurement error (APE) was calculated. Pearson correlation coefficients were calculated between CB_hand and echocardiographic measurements. Results CB parameters determined by CB_auto yielded excellent reliability (intraclass correlation coefficient > 0.98). The 95% limits of agreement for the cardiothoracic ratio were 0.00 +/- 0.04% without systemic bias. The differences between parameters determined by CB_auto and CB_hand as defined by the APE were < 10% for all parameters except for carinal angle and left atrial appendage. In the public dataset, all CB parameters were successfully drawn in 124 of 132 CXRs (93.9%). All CB parameters were significantly greater in VHD than in normal controls (all p < 0.05). All CB parameters showed significant correlations (p < 0.05) with echocardiographic measurements. Conclusions The CB_auto system empowered by deep learning algorithm provided highly reliable CB measurements that could be useful not only in daily clinical practice but also for research purposes. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | HYBRID ITERATIVE RECONSTRUCTION | - |
dc.subject | FILTERED BACK-PROJECTION | - |
dc.subject | CARDIOTHORACIC RATIO | - |
dc.subject | DOSE SETTINGS | - |
dc.title | A deep learning-based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sun Won | - |
dc.identifier.doi | 10.1007/s00330-021-08296-9 | - |
dc.identifier.scopusid | 2-s2.0-85116986445 | - |
dc.identifier.wosid | 000706927400008 | - |
dc.identifier.bibliographicCitation | EUROPEAN RADIOLOGY, v.32, no.3, pp.1558 - 1569 | - |
dc.relation.isPartOf | EUROPEAN RADIOLOGY | - |
dc.citation.title | EUROPEAN RADIOLOGY | - |
dc.citation.volume | 32 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1558 | - |
dc.citation.endPage | 1569 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | HYBRID ITERATIVE RECONSTRUCTION | - |
dc.subject.keywordPlus | FILTERED BACK-PROJECTION | - |
dc.subject.keywordPlus | CARDIOTHORACIC RATIO | - |
dc.subject.keywordPlus | DOSE SETTINGS | - |
dc.subject.keywordAuthor | Heart valve diseases | - |
dc.subject.keywordAuthor | Radiography | - |
dc.subject.keywordAuthor | Cardiovascular system | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | Deep learning | - |
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