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However, no method has been published for quantitatively estimating PAWP from such radiographs. We hypothesized that a regression CNN, an alternative type of deep learning, could be a useful tool for quantitatively estimating PAWP in cardiovascular diseases. We retrospectively enrolled 936 patients with cardiovascular diseases who had undergone right heart catheterization (RHC) and chest radiography and estimated PAWP by constructing a regression CNN based on the VGG16 model. We randomly categorized 80% of the data as training data (training group, <jats:italic>n<\/jats:italic>\u2009=\u2009748) and 20% as test data (test group, <jats:italic>n<\/jats:italic>\u2009=\u2009188). Moreover, we tuned the learning rate\u2014one of the model parameters\u2014by 5-hold cross-validation of the training group. Correlations between PAWP measured by RHC [ground truth (GT) PAWP] and PAWP derived from the regression CNN (estimated PAWP) were tested. To visualize how the regression CNN assessed the images, we created a regression activation map (RAM), a visualization technique for regression CNN. Estimated PAWP correlated significantly with GT PAWP in both the training (<jats:italic>r<\/jats:italic>\u2009=\u20090.76, <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001) and test group (<jats:italic>r<\/jats:italic>\u2009=\u20090.62, <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001). Bland\u2013Altman plots found a mean (SEM) difference between GT and estimated PAWP of \u2212\u00a00.23 (0.16) mm Hg in the training and \u2212\u00a00.05 (0.41) mm Hg in the test group. The RAM showed that our regression CNN model estimated high PAWP by focusing on the cardiomegaly and pulmonary congestion. In the test group, the area under the curve (AUC) for detecting elevated PAWP (\u2265\u200918\u00a0mm Hg) produced by the regression CNN model was similar to the AUC of an experienced cardiologist (0.86 vs 0.83, respectively; <jats:italic>P<\/jats:italic>\u2009=\u20090.24). This proof-of-concept study shows that regression CNN can quantitatively estimate PAWP from standard chest radiographs in cardiovascular diseases.<\/jats:p>","DOI":"10.1007\/s00380-022-02043-w","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T11:02:36Z","timestamp":1645959756000},"page":"1387-1394","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Quantitative estimation of pulmonary artery wedge pressure from chest radiographs by a regression convolutional neural network"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8193-9575","authenticated-orcid":false,"given":"Yuki","family":"Saito","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuto","family":"Omae","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daisuke","family":"Fukamachi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koichi","family":"Nagashima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saki","family":"Mizobuchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yohei","family":"Kakimoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Toyotani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasuo","family":"Okumura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,27]]},"reference":[{"key":"2043_CR1","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1016\/j.jacc.2013.11.053","volume":"63","author":"AP Ambrosy","year":"2014","unstructured":"Ambrosy AP, Fonarow GC, Butler J, Chioncel O, Greene SJ, Vaduganathan M, Nodari S, Lam CSP, Sato N, Shah AN, Gheorghiade M (2014) The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries. 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