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

Autor:	Li YJ; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China., Zhang LG; Laboratory for Automated Reasoning and Programming, Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, China.; University of Chinese Academy of Sciences, Beijing, China., Zhi HY; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China., Zhong KH; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, Chongqing, China., He WQ; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China., Chen Y; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China., Yang ZY; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China., Chen L; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China., Bai XH; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China., Qin XL; Laboratory for Automated Reasoning and Programming, Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, China., Li DF; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China., Wang DD; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China., Gu JT; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China., Ning JL; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China., Lu KZ; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China., Zhang J; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, Chongqing, China., Xia ZY; Department of Anaesthesiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China., Chen YW; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, Chongqing, China., Yi B; Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China.
Jazyk:	angličtina
Zdroj:	Annals of translational medicine [Ann Transl Med] 2020 Oct; Vol. 8 (19), pp. 1219.
DOI:	10.21037/atm-20-1806
Abstrakt:	Background: 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. Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-20-1806). Dr. YJL, LGZ, HYZ, KHZ, ZYY, KZL, JZ, YWC, BY have a patent 202010324328.5 pending. The other authors have no conflicts of interest to declare. (2020 Annals of Translational Medicine. All rights reserved.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu