Journal Article

Real-time non-invasive hemoglobin prediction using deep learning-enabled smartphone imaging

Yi, Bin, Liu, Xiang, Zhu, Yiziting, Chen, Yuwen, Hu, Xiaoyan

2024

Overview

Background Accurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Traditional invasive methods are inconvenient and not suitable for rapid, point-of-care testing. Moreover, current models, due to their complex parameters, are not well-suited for mobile medical settings, which limits the ability to conduct frequent and rapid testing. This study aims to introduce a novel, compact, and efficient system that leverages deep learning and smartphone technology to accurately estimate hemoglobin levels, thereby facilitating rapid and accessible medical assessments. Methods The study employed a smartphone application to capture images of the eye, which were subsequently analyzed by a deep neural network trained on data from invasive blood test data. Specifically, the EGE-Unet model was utilized for eyelid segmentation, while the DHA(C3AE) model was employed for hemoglobin level prediction. The performance of the EGE-Unet was evaluated using statistical metrics including mean intersection over union (MIOU), F1 Score, accuracy, specificity, and sensitivity. The DHA(C3AE) model’s performance was assessed using mean absolute error (MAE), mean-square error (MSE), root mean square error (RMSE), and R^2. Results The EGE-Unet model demonstrated robust performance in eyelid segmentation, achieving an MIOU of 0.78, an F1 Score of 0.87, an accuracy of 0.97, a specificity of 0.98, and a sensitivity of 0.86. The DHA(C3AE) model for hemoglobin level prediction yielded promising outcomes with an MAE of 1.34, an MSE of 2.85, an RMSE of 1.69, and an R^2 of 0.34. The overall size of the model is modest at 1.08 M, with a computational complexity of 0.12 FLOPs (G). Conclusions This system presents a groundbreaking approach that eliminates the need for supplementary devices, providing a cost-effective, swift, and accurate method for healthcare professionals to enhance treatment planning and improve patient care in perioperative environments. The proposed system has the potential to enable frequent and rapid testing of hemoglobin levels, which can be particularly beneficial in mobile medical settings. Trial Registration The clinical trial was registered on the Chinese Clinical Trial Registry (No. ChiCTR2100044138) on 20/02/2021.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Algorithms / Analysis / Anemia / Artificial intelligence / Artificial neural networks / Automatic / Blood / Blood levels / Clinical trials / Complexity / Data collection / Deep Learning / Eyelid / Female / Health Informatics / Hemoglobin / Hemoglobins - analysis / Humans / Image processing / Image segmentation / Information Systems and Communication Service / Machine learning / Male / Management of Computing and Information Systems / Medical imaging equipment / Medical research / Medicine / Medicine & Public Health / Methods / Middle Aged / Mobile Applications / Neural networks / Non-invasive prediction / Patients / Performance evaluation / Predictions / Research methodology / Root-mean-square errors / Segmentation / Smart phones / Smartphone / Smartphones / Statistical models / Technology assessment