{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T17:31:21Z","timestamp":1790184681294,"version":"4.1.0"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T00:00:00Z","timestamp":1691020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Non-invasive methods for hemoglobin (Hb) monitoring can provide additional and relatively precise information between invasive measurements of Hb to help doctors' decision-making. We aimed to develop a new method for Hb monitoring based on mask R-CNN and MobileNetV3 with eye images as input.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Surgical patients from our center were enrolled. After image acquisition and pre-processing, the eye images, the manually selected palpebral conjunctiva, and features extracted, respectively, from the two kinds of images were used as inputs. A combination of feature engineering and regression, solely MobileNetV3, and a combination of mask R-CNN and MobileNetV3 were applied for model development. The model's performance was evaluated using metrics such as R<jats:sup>2<\/jats:sup>, explained variance score (EVS), and mean absolute error (MAE).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 1,065 original images were analyzed. The model's performance based on the combination of mask R-CNN and MobileNetV3 using the eye images achieved an R<jats:sup>2<\/jats:sup>, EVS, and MAE of 0.503 (95% CI, 0.499\u20130.507), 0.518 (95% CI, 0.515\u20130.522) and 1.6 g\/dL (95% CI, 1.6\u20131.6 g\/dL), which was similar to that based on MobileNetV3 using the manually selected palpebral conjunctiva images (R<jats:sup>2<\/jats:sup>: 0.509, EVS:0.516, MAE:1.6 g\/dL).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We developed a new and automatic method for Hb monitoring to help medical staffs' decision-making with high efficiency, especially in cases of disaster rescue, casualty transport, and so on.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2023.1151996","type":"journal-article","created":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T14:50:12Z","timestamp":1691074212000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["A new, feasible, and convenient method based on semantic segmentation and deep learning for hemoglobin monitoring"],"prefix":"10.3389","volume":"10","author":[{"given":"Xiao-yan","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-jie","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Shu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ai-lin","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-zhu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xian-feng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong-shuai","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-fang","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-yong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-yong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin-quan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-wen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,8,3]]},"reference":[{"key":"B1","author":"Miller","year":"2016","journal-title":"Patient Blood Management: Transfusion Therapy. Miller's Anesthesia, 8\/E"},{"key":"B2","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/s11606-006-5004-x","article-title":"The relation of conjunctival pallor to the presence of anemia","volume":"12","author":"Sheth TN","year":"1997","journal-title":"J Gen Intern Med."},{"key":"B3","doi-asserted-by":"publisher","first-page":"997","DOI":"10.3390\/electronics9060997","article-title":"Novel biased normalized cuts approach for the automatic segmentation of the conjunctiva","volume":"9","author":"Dimauro","year":"2020","journal-title":"Electronics."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.3390\/electronics9081309","article-title":"Semantic segmentation of conjunctiva region for non-invasive anemia detection applications","volume":"9","author":"Kasiviswanathan","year":"2020","journal-title":"Electronics."},{"key":"B5","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/BECITHCON48839.2019.9063188","article-title":"Comparative study between decision tree, svm and knn to predict anaemic condition","volume":"5","author":"Noor","year":"2019","journal-title":"BECITHCON."},{"key":"B6","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41551-019-0487-z","article-title":"Detection of anaemia from retinal fundus images via deep learning","volume":"4","author":"Mitani","year":"2020","journal-title":"Nat Biomed Eng."},{"key":"B7","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.cmpb.2016.08.025","article-title":"Examining palpebral conjunctiva for anemia assessment with image processing methods","volume":"137","author":"Chen","year":"2016","journal-title":"Comput Methods Programs Biomed."},{"key":"B8","author":"Gerson Delgado-Rivera","year":"2018","journal-title":"Method for the Automatic Segmentation of the Palpebral Conjunctiva using Image Processing. IEEE International Conference on Automation\/XXIII Congress of the Chilean Association of Automatic Control (ICA-ACCA)."},{"key":"B9","doi-asserted-by":"publisher","first-page":"7533760","DOI":"10.1109\/MeMeA.2016.7533760","article-title":"A novel approach to evaluate blood parameters using computer vision techniques","author":"Bevilacqua","year":"2016","journal-title":"IEEE International Symposium on Medical Measurements and Applications, MeMeA 2016 - Proceedings"},{"key":"B10","doi-asserted-by":"publisher","first-page":"46968","DOI":"10.1109\/ACCESS.2018.2867110","article-title":"A new method and a non-invasive device to estimate anemia based on digital images of the conjunctiva","volume":"6","author":"Dimauro","year":"2018","journal-title":"IEEE Access."},{"key":"B11","doi-asserted-by":"publisher","first-page":"113488","DOI":"10.1109\/ACCESS.2019.2932274","article-title":"Detecting clinical signs of anaemia from digital images of the palpebral conjunctiva","volume":"7","author":"Dimauro","year":"2019","journal-title":"IEEE Access."},{"key":"B12","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.jemermed.2007.02.011","article-title":"Non-invasive determination of hemoglobin by digital photography of palpebral conjunctiva","volume":"33","author":"Suner","year":"2007","journal-title":"J Emerg Med."},{"key":"B13","unstructured":"Automatic segmentation of relevant sections of the conjunctiva for non-invasive anemia detection15\n            DimauroG\n            BaldariL\n            CaivanoD\n            ColucciG\n            GirardiF\n          SplitIEEEInternational Conference on Smart and Sustainable Technologies (SpliTech).2018"},{"key":"B14","author":"Kaiming He","year":"2017","journal-title":"Mask R-CNN. IEEE International Conference on Computer Vision (ICCV)"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"B16","first-page":"936","author":"Lin","year":"2017","journal-title":"Feature Pyramid Networks for Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1109\/ICCV.2019.00140","article-title":"Searching for MobileNetV3","author":"Howard","year":"2019","journal-title":"IEEE International Conference on Computer Vision (ICCV)."},{"key":"B18","doi-asserted-by":"publisher","first-page":"3167","DOI":"10.21105\/joss.03167","article-title":"Visualizations with statistical details: the 'ggstatsplot' approach","volume":"6","author":"Patil","year":"2021","journal-title":"J Open Source Softw."},{"key":"B19","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-24277-4","author":"Wickham","year":"2016","journal-title":"ggplot2: Elegant Graphics for Data Analysis"},{"key":"B20","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1371\/journal.pone.0153286","volume":"11","author":"Collings","year":"2016","journal-title":"Non-invasive detection of anaemia using digital photographs of the conjunctiva. PloS ONE"},{"key":"B21","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1002\/ima.22359","article-title":"Neural network based non-invasive method to detect anemia from images of eye conjunctiva","volume":"30","author":"Jain","year":"2019","journal-title":"Int J Imaging Syst Technol."},{"key":"B22","first-page":"2611","article-title":"A Smart (phone) solution: an effective tool for screening anaemia - correlation with conjunctiva pallor and haemoglobin levels","volume":"14","author":"Muthalagu","year":"2018","journal-title":"TAGA J."},{"key":"B23","unstructured":"Portable system for the prediction of anemia based on the ocular conjunctiva using Artificial Intelligence\n            Saldivar-EspinozaB\n            N\u00fa\u00f1ez-Fern\u00e1ndezD\n            Porras-BarrientosF\n            Alva-MantariA\n            LeslieLS\n            ZimicM\n          arXiv [Preprint].2019"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2023.1151996\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T14:17:09Z","timestamp":1693837029000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2023.1151996\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,3]]},"references-count":23,"alternative-id":["10.3389\/fmed.2023.1151996"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2023.1151996","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,3]]},"article-number":"1151996"}}