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A new, feasible, and convenient method based on semantic segmentation and deep learning for hemoglobin monitoring
Hu, Xiao-Yan1; Li, Yu-Jie1; Shu, Xin1; Song, Ai-Lin1; Liang, Hao1; Sun, Yi-Zhu1; Wu, Xian-Feng1; Li, Yong-Shuai1; Tan, Li-Fang1; Yang, Zhi-Yong1
2023-08-03
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 R-2, 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 R-2, 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 (R-2: 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.
关键词continuous hemoglobin monitoring deep learning semantic segmentation mask R-CNN MobileNetV3
DOI10.3389/fmed.2023.1151996
发表期刊FRONTIERS IN MEDICINE
卷号10页码:9
通讯作者Chen, Yu-Wen(chenyuwen@cigit.ac.cn) ; Yi, Bin(yibin1974@163.com)
收录类别SCI
WOS记录号WOS:001048942700001
语种英语