Dataset Information

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

ABSTRACT:

Objective

Non-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.

Methods

Surgical 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).

Results

A 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).

Conclusion

We 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.

SUBMITTER: Hu XY

PROVIDER: S-EPMC10435289 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

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

Hu Xiao-Yan XY Li Yu-Jie YJ Shu Xin X Song Ai-Lin AL Liang Hao H Sun Yi-Zhu YZ Wu Xian-Feng XF Li Yong-Shuai YS Tan Li-Fang LF Yang Zhi-Yong ZY Yang Chun-Yong CY Xu Lin-Quan LQ Chen Yu-Wen YW Yi Bin B

Frontiers in medicine 20230803

<h4>Objective</h4>Non-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.<h4>Methods</h4>Surgical patients from our center were enrolled. After image acquisition and pre-processing, the eye images, the manually selected palpebral conjunctiva, and features extract ...[more]

PMID: 37601798

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Dataset Information

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

Objective

Methods

Results

Conclusion

Publications

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

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