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  1. 30 大学院医学系研究科・医学部
  2. 30D 学位論文
  3. 博士論文 本文
  4. 2023年度

Prediction of Pulmonary to Systemic Flow Ratio in Patients With Congenital Heart Disease Using Deep Learning-Based Analysis of Chest Radiographs

http://hdl.handle.net/10076/0002000082
http://hdl.handle.net/10076/0002000082
fe8f0606-25d9-4d47-92f6-f21566869942
名前 / ファイル ライセンス アクション
2023DM0704.pdf 2023DM0704 (1.8MB)
Item type 学位論文 / Thesis or Dissertation(1)
公開日 2023-09-08
タイトル
タイトル Prediction of Pulmonary to Systemic Flow Ratio in Patients With Congenital Heart Disease Using Deep Learning-Based Analysis of Chest Radiographs
言語 en
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_db06
資源タイプ doctoral thesis
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
著者 鳥羽, 修平

× 鳥羽, 修平

en Toba, Shuhei

ja-Kana トバ, シュウヘイ

ja 鳥羽, 修平

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内容記述タイプ Abstract
内容記述 IMPORTANCE Chest radiography is a useful noninvasive modality to evaluate pulmonary blood flow status in patients with congenital heart disease. However, the predictive value of chest radiography is limited by the subjective and qualitive nature of the interpretation. Recently, deep learning has been used to analyze various images, but it has not been applied to analyzing chest radiographs in such patients.
OBJECTIVE To develop and validate a quantitative method to predict the pulmonary to systemic flow ratio from chest radiographs using deep learning.
DESIGN, SETTING, AND PARTICIPANTS This retrospective observational study included 1031 cardiac catheterizations performed for 657 patients from January 1, 2005, to April 30, 2019, at a tertiary center. Catheterizations without the Fick-derived pulmonary to systemic flow ratio or chest radiography performed within 1 month before catheterization were excluded. Seventy-eight patients (100 catheterizations) were randomly assigned for evaluation. A deep learning model that predicts the pulmonary to systemic flow ratio from chest radiographs was developed using the method of transfer learning.
MAIN OUTCOMES AND MEASURES Whether the model can predict the pulmonary to systemic flow ratio from chest radiographs was evaluated using the intraclass correlation coefficient and Bland-Altman analysis. The diagnostic concordance rate was compared with 3 certified pediatric cardiologists. The diagnostic performance for a high pulmonary to systemic flow ratio of 2.0 or more was evaluated using cross tabulation and a receiver operating characteristic curve.
RESULTS The study included 1031 catheterizations in 657 patients (522 males [51%]; median age, 3.4 years [interquartile range, 1.2-8.6 years]), in whom the mean (SD) Fick-derived pulmonary to systemic flow ratio was 1.43 (0.95). Diagnosis included congenital heart disease in 1008 catheterizations (98%). The intraclass correlation coefficient for the Fick-derived and deep learning–derived pulmonary to systemic flow ratio was 0.68, the log-transformed bias was 0.02, and the log-transformed precision was 0.12. The diagnostic concordance rate of the deep learning model was significantly higher than that of the experts (correctly classified 64 of 100 vs 49 of 100 chest radiographs; P = .02 [McNemar test]). For detecting a high pulmonary to systemic flow ratio, the sensitivity of the deep learning model was 0.47, the specificity was 0.95, and the area under the receiver operating curve was 0.88.
CONCLUSIONS AND RELEVANCE The present investigation demonstrated that deep learning–based analysis of chest radiographs predicted the pulmonary to systemic flow ratio in patients with congenital heart disease. These findings suggest that the deep learning–based approach may confer an objective and quantitative evaluation of chest radiographs in the congenital heart disease clinic.
言語 en
内容記述
内容記述タイプ Other
内容記述 本文/Department of Thoracic and Cardiovascular Surgery, Mie University Graduate School of Medicine, Tsu, Mie, Japan
内容記述
内容記述タイプ Other
内容記述 9p
書誌情報
発行日 2023-07-19
DOI
識別子タイプ DOI
関連識別子 10.1001/jamacardio.2019.5620
フォーマット
内容記述タイプ Other
内容記述 application/pdf
出版者
出版者 三重大学
出版者(ヨミ)
値 ミエダイガク
学位名
学位名 博士(医学)
学位授与機関
学位授与機関識別子Scheme kakenhi
学位授与機関識別子 14101
学位授与機関名 三重大学
学位授与年月日
学位授与年月日 2023-07-19
学位授与番号
学位授与番号 乙医学第1090号
ノート
資源タイプ(三重大)
値 Doctoral Dissertation / 博士論文
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