Frontiers in Medicine (Aug 2023)

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

  • Xiao-yan Hu,
  • Yu-jie Li,
  • Xin Shu,
  • Ai-lin Song,
  • Hao Liang,
  • Yi-zhu Sun,
  • Xian-feng Wu,
  • Yong-shuai Li,
  • Li-fang Tan,
  • Zhi-yong Yang,
  • Chun-yong Yang,
  • Lin-quan Xu,
  • Yu-wen Chen,
  • Bin Yi

DOI
https://doi.org/10.3389/fmed.2023.1151996
Journal volume & issue
Vol. 10

Abstract

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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.

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