徳島大学 教育・研究者情報データベース(EDB)

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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3. (英) Tsuji T

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4. (英) Kotoku J

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5. 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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(英) Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray

(英) Accurate diagnosis of pulmonary hypertension (PH) is crucial to ensure that patients receive timely treatment. We hypothesized that application of artificial intelligence (AI) to the chest X-ray (CXR) could identify elevated pulmonary artery pressure (PAP) and stratify the risk of heart failure hospitalization with PH. We retrospectively enrolled a total of 900 consecutive patients with suspected PH. We trained a convolutional neural network to identify patients with elevated PAP (> 20 mmHg) as the actual value of PAP. The endpoints in this study were admission or occurrence of heart failure with elevated PAP. In an independent evaluation set for detection of elevated PAP, the area under curve (AUC) by the AI algorithm was significantly higher than the AUC by measurements of CXR images and human observers (0.71 vs. 0.60 and vs. 0.63, all p < 0.05). In patients with AI predicted PH had 2-times the risk of heart failure with PH compared with those without AI predicted PH. This preliminary work suggests that applying AI to the CXR in high risk groups has limited performance when used alone in identifying elevated PAP. We believe that this report can serve as an impetus for a future large study.

1. (英) Article.ELocationID: 10.1038/s41598-020-76359-w

2. (英) Article.PublicationTypeList.PublicationType: Journal Article