A new, feasible, and convenient method based on semantic segmentation and deep learning for hemoglobin monitoring

Frontiers in Medicine

Medicine

Lin-quan Xu¹  Yu-wen Chen¹  Yu-jie Li²  Yong-shuai Li²  Xian-feng Wu²  Xiao-yan Hu²  Zhi-yong Yang²  Xin Shu²  Li-fang Tan²  Bin Yi²  Ai-lin Song²  Chun-yong Yang²  Yi-zhu Sun²  Hao Liang² 

[1] Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, Chongqing, China;Department of Anesthesiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China;

关键词: continuous hemoglobin monitoring;    deep learning;    semantic segmentation;    mask R-CNN;    MobileNetV3;   

DOI  :  10.3389/fmed.2023.1151996

 received in 2023-02-01, accepted in 2023-07-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

ObjectiveNon-invasive methods for hemoglobin (Hb) monitoring can provide additional and relatively precise information between invasive measurements of Hb to help doctors' decision-making. We aimed to develop a new method for Hb monitoring based on mask R-CNN and MobileNetV3 with eye images as input.MethodsSurgical patients from our center were enrolled. After image acquisition and pre-processing, the eye images, the manually selected palpebral conjunctiva, and features extracted, respectively, from the two kinds of images were used as inputs. A combination of feature engineering and regression, solely MobileNetV3, and a combination of mask R-CNN and MobileNetV3 were applied for model development. The model's performance was evaluated using metrics such as R2, explained variance score (EVS), and mean absolute error (MAE).ResultsA total of 1,065 original images were analyzed. The model's performance based on the combination of mask R-CNN and MobileNetV3 using the eye images achieved an R2, EVS, and MAE of 0.503 (95% CI, 0.499–0.507), 0.518 (95% CI, 0.515–0.522) and 1.6 g/dL (95% CI, 1.6–1.6 g/dL), which was similar to that based on MobileNetV3 using the manually selected palpebral conjunctiva images (R2: 0.509, EVS:0.516, MAE:1.6 g/dL).ConclusionWe developed a new and automatic method for Hb monitoring to help medical staffs' decision-making with high efficiency, especially in cases of disaster rescue, casualty transport, and so on.

【 授权许可】

Unknown   
Copyright © 2023 Hu, Li, Shu, Song, Liang, Sun, Wu, Li, Tan, Yang, Yang, Xu, Chen and Yi.