| ○種別 (必須): | □ | 学術論文 (審査論文)
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| ○言語 (必須): | □ | 英語
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| ○審査 (推奨): | □ | Peer Review
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| ○組織 (推奨): |
| ○著者 (必須): | 1. | 楠瀬 賢也
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| 2. | (英) Hirata Yukina (日) 平田 有紀奈 (読) ひらた ゆきな
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| ○学籍番号 (推奨): | □ | ****
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| 3. | (英) Yamaguchi Natsumi (日) 山口 夏美 (読) やまぐち なつみ
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| ○学籍番号 (推奨): | □ | ****
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| 4. | (英) Kosaka Y (日) 髙坂 佳孝 (読) こうさか よしたか
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| ○学籍番号 (推奨): | □ | ****
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| 5. | (英) Tsuji T (日) (読)
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| 6. | (英) Kotoku J (日) (読)
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| 7. | 佐田 政隆 ([徳島大学.大学院医歯薬学研究部.医学域.医科学部門.内科系.循環器内科学])
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| ○題名 (必須): | □ | (英) Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images (日)
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| ○要約 (任意): | □ | (英) Stress echocardiography is an emerging tool used to detect exercise-induced pulmonary hypertension (EIPH). However, facilities that can perform stress echocardiography are limited by issues such as cost and equipment. We evaluated the usefulness of a deep learning (DL) approach based on a chest X-ray (CXR) to predict EIPH in 6-min walk stress echocardiography. The study enrolled 142 patients with scleroderma or mixed connective tissue disease with scleroderma features who performed a 6-min walk stress echocardiographic test. EIPH was defined by abnormal cardiac output (CO) responses that involved an increase in mean pulmonary artery pressure (mPAP). We used the previously developed AI model to predict PH and calculated PH probability in this cohort. EIPH defined as ΔmPAP/ΔCO >3.3 and exercise mPAP >25 mmHg was observed in 52 patients, while non-EIPH was observed in 90 patients. The patients with EIPH had a higher mPAP at rest than those without EIPH. The probability of PH based on the DL model was significantly higher in patients with EIPH than in those without EIPH. Multivariate analysis showed that gender, mean PAP at rest, and the probability of PH based on the DL model were independent predictors of EIPH. A model based on baseline parameters (age, gender, and mPAP at rest) was improved by adding the probability of PH predicted by the DL model (AUC: from 0.65 to 0.74; = 0.046). Applying the DL model based on a CXR may have a potential for detection of EIPH in the clinical setting. (日)
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| ○キーワード (推奨): |
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| ○誌名 (必須): | □ | Frontiers in Cardiovascular Medicine (Frontiers Media S.A.)
(eISSN: 2297-055X)
| ○ISSN (任意): | □ | 2297-055X
ISSN: 2297-055X
(eISSN: 2297-055X) Title: Frontiers in cardiovascular medicineTitle(ISO): Front Cardiovasc MedPublisher: Frontiers Media SA (NLM Catalog)
(Scopus)
(CrossRef)
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| ○巻 (必須): | □ | 9
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| ○号 (必須): |
| ○頁 (必須): | □ | 891703 891703
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| ○都市 (任意): |
| ○年月日 (必須): | □ | 西暦 2022年 6月 15日 (令和 4年 6月 15日)
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| ○URL (任意): |
| ○DOI (任意): | □ | 10.3389/fcvm.2022.891703 (→Scopusで検索)
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| ○PMID (任意): | □ | 35783826 (→Scopusで検索)
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| ○備考 (任意): | 1. | (英) Article.ELocationID: 10.3389/fcvm.2022.891703 (日)
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| 2. | (英) Article.PublicationTypeList.PublicationType: Journal Article (日)
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| 3. | (英) KeywordList.Keyword: artificial intelligence (日)
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| 4. | (英) KeywordList.Keyword: connective tissue disease (日)
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| 5. | (英) KeywordList.Keyword: echocardiography (日)
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| 6. | (英) KeywordList.Keyword: exercise pulmonary hypertension (日)
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| 7. | (英) KeywordList.Keyword: scleroderma (SSc) (日)
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| 8. | (英) CoiStatement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (日)
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