Automated blood volume estimation in surgical drains for clinical decision support.

Abstract

Monitoring Jackson Pratt and Hemovac drains plays a crucial role in assessing a patient's recovery and identifying potential postoperative complications. Accurate and regular monitoring of the blood volume in the drain is essential for making decisions about patient care. However, transferring blood to a measuring cup and recording it is a challenging task for both patients and doctors, exposing them to bloodborne pathogens such as the human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV). To automate the recording process with a non-contact approach, we propose an innovative approach that utilizes deep learning techniques to detect a drain in a photograph, compute the blood level in the drain, estimate the blood volume, and display the results on both web and mobile interfaces. Our system employs semantic segmentation on images taken with mobile phones to effectively isolate the blood-filled portion of the drain from the rest of the image and compute the blood volume. These results are then sent to mobile and web applications for convenient access. To validate the accuracy and effectiveness of our system, we collected the Drain Dataset, which consists of 1,004 images taken under various background and lighting conditions. With an average error rate of less than 5% in milliliters, our proposed approach achieves highly accurate blood level detection and estimation, as demonstrated by our trials on this dataset. The system also exhibits robustness to variations in lighting conditions and drain shapes, ensuring its applicability in different clinical scenarios. The proposed automated blood volume estimation system can significantly reduce the time and effort required for manual measurements, enabling healthcare professionals to focus on other critical tasks. The dataset and annotations are available at: https://www.kaggle.com/datasets/ayenahin/liquid-volume-detection-from-drain-images and the code for the web application is available at https://github.com/itsjustaplant/AwesomeProject.git.