Bal Ufuk, Oguz Faruk Enes, Sunnetci Kubilay Muhammed, Alkan Ahmet, Bal Alkan, Akkuş Ebubekir, Erol Halil, Seçkin Ahmet Çağdaş
Department of Electrical and Electronics Engineering, Osmaniye Korkut Ata University, 80000 Osmaniye, Türkiye.
Hassa Vocational School, Hatay Mustafa Kemal University, 31060 Hatay, Türkiye.
Biosensors (Basel). 2025 Jul 25;15(8):485. doi: 10.3390/bios15080485.
Hemoglobin plays a critical role in diagnosing various medical conditions, including infections, trauma, hemolytic disorders, and Mediterranean anemia, which is particularly prevalent in Mediterranean populations. Conventional measurement methods require blood sampling and laboratory analysis, which are often time-consuming and impractical during emergency situations with limited medical infrastructure. Although portable oximeters enable non-invasive hemoglobin estimation, they still require physical contact, posing limitations for individuals with circulatory or dermatological conditions. Additionally, reliance on disposable probes increases operational costs. This study presents a non-contact and automated approach for estimating total hemoglobin levels from facial video data using three-dimensional regression models. A dataset was compiled from 279 volunteers, with synchronized acquisition of facial video and hemoglobin values using a commercial pulse oximeter. After preprocessing, the dataset was divided into training, validation, and test subsets. Three 3D convolutional regression models, including 3D CNN, channel attention-enhanced 3D CNN, and residual 3D CNN, were trained, and the most successful model was implemented in a graphical interface. Among these, the residual model achieved the most favorable performance on the test set, yielding an RMSE of 1.06, an MAE of 0.85, and a Pearson correlation coefficient of 0.73. This study offers a novel contribution by enabling contactless hemoglobin estimation from facial video using 3D CNN-based regression techniques.
血红蛋白在诊断各种医疗状况中起着关键作用，这些状况包括感染、创伤、溶血性疾病以及地中海贫血，地中海贫血在地中海人群中尤为普遍。传统的测量方法需要采血和实验室分析，在医疗基础设施有限的紧急情况下，这往往既耗时又不切实际。尽管便携式血氧仪能够进行非侵入性血红蛋白估计，但它们仍需要身体接触，这对患有循环系统或皮肤病的个体构成了限制。此外，依赖一次性探头会增加运营成本。本研究提出了一种使用三维回归模型从面部视频数据估计总血红蛋白水平的非接触式自动化方法。从279名志愿者中收集了一个数据集，使用商用脉搏血氧仪同步采集面部视频和血红蛋白值。预处理后，将数据集分为训练集、验证集和测试集。训练了三个三维卷积回归模型，包括三维卷积神经网络（3D CNN）、通道注意力增强的三维卷积神经网络和残差三维卷积神经网络，并在图形界面中实现了最成功的模型。其中，残差模型在测试集上取得了最理想的性能，均方根误差（RMSE）为1.06，平均绝对误差（MAE）为0.85，皮尔逊相关系数为0.73。本研究通过使用基于三维卷积神经网络的回归技术从面部视频中实现非接触式血红蛋白估计，做出了新颖的贡献。
