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Real-time and accurate estimation of surgical hemoglobin loss using deep learning-based medical sponges image analysis.
- Source :
- Scientific Reports; 11/20/2023, Vol. 13 Issue 1, p1-8, 8p
- Publication Year :
- 2023
-
Abstract
- Real-time and accurate estimation of surgical hemoglobin (Hb) loss is essential for fluid resuscitation management and evaluation of surgical techniques. In this study, we aimed to explore a novel surgical Hb loss estimation method using deep learning-based medical sponges image analysis. Whole blood samples of pre-measured Hb concentration were collected, and normal saline was added to simulate varying levels of Hb concentration. These blood samples were distributed across blank medical sponges to generate blood-soaked sponges. Eight hundred fifty-one blood-soaked sponges representing a wide range of blood dilutions were randomly divided 7:3 into a training group (n = 595) and a testing group (n = 256). A deep learning model based on the YOLOv5 network was used as the target region extraction and detection, and the three models (Feature extraction technology, ResNet-50, and SE-ResNet50) were trained to predict surgical Hb loss. Mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient (R<superscript>2</superscript>) value, and the Bland–Altman analysis were calculated to evaluate the predictive performance in the testing group. The deep learning model based on SE-ResNet50 could predict surgical Hb loss with the best performance (R<superscript>2</superscript> = 0.99, MAE = 11.09 mg, MAPE = 8.6%) compared with other predictive models, and Bland–Altman analysis also showed a bias of 1.343 mg with narrow limits of agreement (− 29.81 to 32.5 mg) between predictive and actual Hb loss. The interactive interface was also designed to display the real-time prediction of surgical Hb loss more intuitively. Thus, it is feasible for real-time estimation of surgical Hb loss using deep learning-based medical sponges image analysis, which was helpful for clinical decisions and technical evaluation. [ABSTRACT FROM AUTHOR]
- Subjects :
- IMAGE analysis
DIAGNOSTIC imaging
HEMOGLOBINS
DEEP learning
FEATURE extraction
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 13
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 173764554
- Full Text :
- https://doi.org/10.1038/s41598-023-42572-6