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

文献类型：期刊论文

作者Li, Yu-Jie2; Zhang, Li-Ge3,4; Zhi, Hong-Yu2; Zhong, Kun-Hua5; He, Wen-Quan2; Chen, Yang2; Yang, Zhi-Yong2; Chen, Lin2; Bai, Xue-Hong2; Qin, Xiao-Lin3
刊名ANNALS OF TRANSLATIONAL MEDICINE
出版日期2020-10-01
卷号8期号:19页码:14
关键词Intra-operative blood loss intra-operative haemoglobin loss densely connected convolutional networks feature extraction technology
ISSN号2305-5839
DOI10.21037/atm-20-1806
通讯作者Chen, Yu-Wen(chenyuwen@cigit.ac.cn) ; Yi, Bin(yibin1974@163.com)
英文摘要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.
资助项目National Key R&D Program of China[2018YFC0116702] ; National Key R&D Program of China[2018YFC0116704] ; National Natural Science Foundation of China[81870422] ; National Natural Science Foundation of China[81600035] ; Medical Innovation Capacity Improvement Program for Medical Staff of the First Affiliated Hospital of the Third Military Medical University[SWH2018QNKJ-27] ; Technology Innovation and Application Research and Development Project of Chongqing City[cstc2019jscx-msxmX0237]
WOS研究方向Oncology ; Research & Experimental Medicine
语种英语
WOS记录号WOS:000581630100013
出版者AME PUBL CO
源URL[http://119.78.100.138/handle/2HOD01W0/12258]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Chen, Yu-Wen; Yi, Bin
作者单位1.Univ Hong Kong, Li Ka Shing Fac Med, Dept Anaesthesiol, Hong Kong, Peoples R China
2.Army Med Univ, Affiliated Hosp 1, Third Mil Med Univ, Dept Anaesthesiol,Southwest Hosp, Chongqing, Peoples R China
3.Chinese Acad Sci, Chengdu Inst Comp Applicat, Lab Automated Reasoning & Programming, Chengdu, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing, Peoples R China
推荐引用方式
GB/T 7714		 Li, Yu-Jie,Zhang, Li-Ge,Zhi, Hong-Yu,et al. A better method for the dynamic, precise estimating of blood/haemoglobin loss based on deep learning of artificial intelligence[J]. ANNALS OF TRANSLATIONAL MEDICINE,2020,8(19):14.
APA Li, Yu-Jie.,Zhang, Li-Ge.,Zhi, Hong-Yu.,Zhong, Kun-Hua.,He, Wen-Quan.,...&Yi, Bin.(2020).A better method for the dynamic, precise estimating of blood/haemoglobin loss based on deep learning of artificial intelligence.ANNALS OF TRANSLATIONAL MEDICINE,8(19),14.
MLA Li, Yu-Jie,et al."A better method for the dynamic, precise estimating of blood/haemoglobin loss based on deep learning of artificial intelligence".ANNALS OF TRANSLATIONAL MEDICINE 8.19(2020):14.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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