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Article: A better method for the dynamic, precise estimating of blood/ haemoglobin loss based on deep learning of artificial intelligence

TitleA better method for the dynamic, precise estimating of blood/ haemoglobin loss based on deep learning of artificial intelligence
Authors
KeywordsIntra-operative blood loss
intra-operative haemoglobin loss
densely connected convolutional networks
feature extraction technology
Issue Date2020
PublisherAME Publishing Company. The Journal's web site is located at http://atm.amegroups.com/about
Citation
Annals of Translational Medicine, 2020, v. 8 n. 19, p. article no. 1219 How to Cite?
AbstractBackground: Dynamic and precise estimation of blood loss (EBL) is quite important for perioperative management. To date, the Triton System, based on feature extraction technology (FET), has been applied to estimate intra-operative haemoglobin (Hb) loss but is unable to directly assess the amount of blood loss. We aimed to develop a method for the dynamic and precise EBL and estimate Hb loss (EHL) based on artificial intelligence (AI). Methods: We collected surgical patients’ non-recycled blood to generate blood-soaked sponges at a set gradient of volume. After image acquisition and preprocessing, FET and densely connected convolutional networks (DenseNet) were applied for EBL and EHL. The accuracy was evaluated using R2, the mean absolute error (MAE), the mean square error (MSE), and the Bland-Altman analysis. Results: For EBL, the R2, MAE and MSE for the method based on DenseNet were 0.966 (95% CI: 0.962–0.971), 0.186 (95% CI: 0.167–0.207) and 0.096 (95% CI: 0.084–0.109), respectively. For EHL, the R2, MAE and MSE for the method based on DenseNet were 0.941 (95% CI: 0.934–0.948), 0.325 (95% CI: 0.293–0.355) and 0.284 (95% CI: 0.251–0.317), respectively. The accuracies of EBL and EHL based on DenseNet were more satisfactory than that of FET. Bland-Altman analysis revealed a bias of 0.02 ml with narrow limits of agreement (LOA) (−0.47 to 0.52 mL) and of 0.05 g with narrow LOA (−0.87 to 0.97 g) between the methods based on DenseNet and actual blood loss and Hb loss. Conclusions: We developed a simpler and more accurate AI-based method for EBL and EHL, which may be more fit for surgeries primarily using sponges and with a small to medium amount of blood loss.
Persistent Identifierhttp://hdl.handle.net/10722/294615
ISSN
2020 Impact Factor: 3.932
2019 SCImago Journal Rankings: 1.089
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, YJ-
dc.contributor.authorZhang, LG-
dc.contributor.authorZhi, HY-
dc.contributor.authorZhong, KH-
dc.contributor.authorHe, WQ-
dc.contributor.authorChen, Y-
dc.contributor.authorYang, ZY-
dc.contributor.authorChen, L-
dc.contributor.authorBai, XH-
dc.contributor.authorQin, XL-
dc.contributor.authorLi, DF-
dc.contributor.authorWang, DD-
dc.contributor.authorGu, JT-
dc.contributor.authorNing, JL-
dc.contributor.authorLU, KZ-
dc.contributor.authorZhang, J-
dc.contributor.authorXia, ZY-
dc.contributor.authorChen, YB-
dc.contributor.authorYi, B-
dc.date.accessioned2020-12-08T07:39:28Z-
dc.date.available2020-12-08T07:39:28Z-
dc.date.issued2020-
dc.identifier.citationAnnals of Translational Medicine, 2020, v. 8 n. 19, p. article no. 1219-
dc.identifier.issn2305-5839-
dc.identifier.urihttp://hdl.handle.net/10722/294615-
dc.description.abstractBackground: Dynamic and precise estimation of blood loss (EBL) is quite important for perioperative management. To date, the Triton System, based on feature extraction technology (FET), has been applied to estimate intra-operative haemoglobin (Hb) loss but is unable to directly assess the amount of blood loss. We aimed to develop a method for the dynamic and precise EBL and estimate Hb loss (EHL) based on artificial intelligence (AI). Methods: We collected surgical patients’ non-recycled blood to generate blood-soaked sponges at a set gradient of volume. After image acquisition and preprocessing, FET and densely connected convolutional networks (DenseNet) were applied for EBL and EHL. The accuracy was evaluated using R2, the mean absolute error (MAE), the mean square error (MSE), and the Bland-Altman analysis. Results: For EBL, the R2, MAE and MSE for the method based on DenseNet were 0.966 (95% CI: 0.962–0.971), 0.186 (95% CI: 0.167–0.207) and 0.096 (95% CI: 0.084–0.109), respectively. For EHL, the R2, MAE and MSE for the method based on DenseNet were 0.941 (95% CI: 0.934–0.948), 0.325 (95% CI: 0.293–0.355) and 0.284 (95% CI: 0.251–0.317), respectively. The accuracies of EBL and EHL based on DenseNet were more satisfactory than that of FET. Bland-Altman analysis revealed a bias of 0.02 ml with narrow limits of agreement (LOA) (−0.47 to 0.52 mL) and of 0.05 g with narrow LOA (−0.87 to 0.97 g) between the methods based on DenseNet and actual blood loss and Hb loss. Conclusions: We developed a simpler and more accurate AI-based method for EBL and EHL, which may be more fit for surgeries primarily using sponges and with a small to medium amount of blood loss.-
dc.languageeng-
dc.publisherAME Publishing Company. The Journal's web site is located at http://atm.amegroups.com/about-
dc.relation.ispartofAnnals of Translational Medicine-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectIntra-operative blood loss-
dc.subjectintra-operative haemoglobin loss-
dc.subjectdensely connected convolutional networks-
dc.subjectfeature extraction technology-
dc.titleA better method for the dynamic, precise estimating of blood/ haemoglobin loss based on deep learning of artificial intelligence-
dc.typeArticle-
dc.identifier.emailXia, ZY: zyxia@hkucc.hku.hk-
dc.identifier.authorityXia, ZY=rp00532-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.21037/atm-20-1806-
dc.identifier.pmid33178751-
dc.identifier.pmcidPMC7607084-
dc.identifier.hkuros320394-
dc.identifier.volume8-
dc.identifier.issue19-
dc.identifier.spagearticle no. 1219-
dc.identifier.epagearticle no. 1219-
dc.identifier.isiWOS:000581630100013-
dc.publisher.placeHong Kong-

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