一种基于人工智能深度学习的动态、精确估算血液/血红蛋白损失的更好方法。

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

作者信息

Li Yu-Jie, Zhang Li-Ge, Zhi Hong-Yu, Zhong Kun-Hua, He Wen-Quan, Chen Yang, Yang Zhi-Yong, Chen Lin, Bai Xue-Hong, Qin Xiao-Lin, Li Dan-Feng, Wang Dan-Dan, Gu Jian-Teng, Ning Jiao-Lin, Lu Kai-Zhi, Zhang Ju, Xia Zheng-Yuan, Chen Yu-Wen, Yi Bin

机构信息

Department of Anaesthesiology, Southwest Hospital, Third Military Medical University (First Affiliated Hospital of Army Medical University), Chongqing, China.

Laboratory for Automated Reasoning and Programming, Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, China.

出版信息

Ann Transl Med. 2020 Oct;8(19):1219. doi: 10.21037/atm-20-1806.

DOI:10.21037/atm-20-1806

PMID:33178751

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7607084/

Abstract

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.

摘要

背景

动态精确估计失血量（EBL）对围手术期管理非常重要。迄今为止，基于特征提取技术（FET）的Triton系统已被用于估计术中血红蛋白（Hb）损失，但无法直接评估失血量。我们旨在开发一种基于人工智能（AI）的动态精确EBL方法并估计Hb损失（EHL）。

方法

我们收集手术患者的非回收血液，以设定的体积梯度生成浸血海绵。在图像采集和预处理后，将FET和密集连接卷积网络（DenseNet）应用于EBL和EHL。使用R2、平均绝对误差（MAE）、均方误差（MSE）和Bland-Altman分析评估准确性。

结果

对于EBL，基于DenseNet的方法的R2、MAE和MSE分别为0.966（95%CI：0.962 - 0.971）、0.186（95%CI：0.167 - 0.207）和0.096（95%CI：0.084 - 0.109）。对于EHL，基于DenseNet的方法的R2、MAE和MSE分别为0.941（95%CI：0.934 - 0.948）、0.325（95%CI：0.293 - 0.355）和0.284（95%CI：0.251 - 0.317）。基于DenseNet的EBL和EHL的准确性比FET更令人满意。Bland-Altman分析显示，基于DenseNet的方法与实际失血量和Hb损失之间的偏差为0.02 ml，一致性界限（LOA）较窄（-0.47至0.52 mL），偏差为0.05 g，LOA较窄（-0.87至0.97 g）。