{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T18:24:47Z","timestamp":1789410287003,"version":"build-2803163510"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Perioperative hemoglobin (Hb) levels can influence tissue metabolism. For clinical physicians, precise Hb concentration greatly contributes to intraoperative blood transfusion. The reduction in Hb during an operation weakens blood's oxygen-carrying capacity and poses threats to multiple systems and organs of the whole body. Patients can die from perioperative anemia. Thus, a timely and accurate non-invasive prediction for patients' Hb content is of enormous significance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>In this study, targeted toward the palpebral conjunctiva images in perioperative patients, a non-invasive model for predicting Hb levels is constructed by means of deep neural semantic segmentation and a convolutional network based on a priori causal knowledge, then an automatic framework was proposed to predict the precise concentration value of Hb. Specifically, according to a priori causal knowledge, the palpebral region was positioned first, and patients' Hb concentration was subjected to regression prediction using a neural network. The model proposed in this study was experimented on using actual medical datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> of the model proposed can reach 0.512, the explained variance score can reach 0.535, and the mean absolute error is 1.521.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>In this study, we proposed to predict the accurate hemoglobin concentration and finally constructed a model using the deep learning method to predict eyelid Hb of perioperative patients based on the a priori casual knowledge.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2022.1079389","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T07:12:40Z","timestamp":1669792360000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Two-stage hemoglobin prediction based on prior 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