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

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

Yu-Jie Li  1 , Li-Ge Zhang  2   3 , Hong-Yu Zhi  1 , Kun-Hua Zhong  4 , Wen-Quan He  1 , Yang Chen  1 , Zhi-Yong Yang  1 , Lin Chen  1 , Xue-Hong Bai  1 , Xiao-Lin Qin  2 , Dan-Feng Li  1 , Dan-Dan Wang  1 , Jian-Teng Gu  1 , Jiao-Lin Ning  1 , Kai-Zhi Lu  1 , Ju Zhang  4 , Zheng-Yuan Xia  5 , Yu-Wen Chen  4 , Bin Yi  1
Affiliations

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

Yu-Jie Li et al. Ann Transl Med. 2020 Oct.

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.

Keywords: Intra-operative blood loss; densely connected convolutional networks; feature extraction technology; intra-operative haemoglobin loss.

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Conflict of interest statement

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.

Figures

Figure 1
Figure 1
Flow diagram for the estimating models based on artificial intelligence, namely, dense network and feature engineering. EBL, estimation of blood loss; EHL, estimation of haemoglobin loss.
Figure 2
Figure 2
Results of concordance among methods based on (A) linear regression; (B) random forest; (C) extreme gradient boosting; (D) dense network for estimating blood loss and the actual data. EBL, estimation of blood loss; LLOA, lower limit of agreement; ULOA, upper limit of agreement; and CI, confidence interval.
Figure 3
Figure 3
Results of concordance among methods based on (A) linear regression; (B) random forest; (C) extreme gradient boosting; (D) dense network for estimating haemoglobin loss and the actual data. EHL, estimation of haemoglobin loss; LLOA, lower limit of agreement; ULOA, upper limit of agreement; CI, confidence interval.
Figure S1
Figure S1
The typical images of blood-soaked sponges with a set gradient of volume.
Figure S2
Figure S2
Blood area images and blood-soaked sponge image.
Figure S3
Figure S3
Part of supercomputing platform.
See this image and copyright information in PMC

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