Detection of hyperglycemia and hypoglycemia using deep learning from facial images obtained with an ai image generator

Abstract

The identification of hyperglycemia and hypoglycemia is paramount in diabetes care, facilitating prompt interventions to mitigate potential health complications. A novel method is introduced for identifying glycemic states using deep learning from facial images generated by artificial intelligence (AI). Specifically, the EfficientB0 model-a pre-trained convolutional neural network (CNN)-is employed, utilizing the transfer learning technique to leverage its learned features for glycemic state classification. The proposed method offers a non-invasive and remote monitoring solution, allowing for convenient glycemic status assessment without the need for invasive procedures or continuous glucose monitoring devices. The experimental results confirm the effectiveness of the proposed method. The achieved accuracy rates, recall rates, and F1-scores validate the model's ability to accurately identify individuals at risk of glycemic abnormalities. The integration of deep learning techniques with facial image analysis holds promise for personalized healthcare solutions tailored to individuals with diabetes, facilitating early detection and intervention for improved glycemic control. By leveraging AI-driven facial image analysis, individuals with diabetes can benefit from early detection and prediction of hyperglycemic and hypoglycemic events, enabling timely interventions and adjustments in treatment regimens. This approach holds promise for improving glycemic control, reducing the risk of acute complications, and enhancing overall quality of life for individuals with diabetes. The non-invasive approach for detecting glycemic states presented in this paper has the potential to revolutionize healthcare management for individuals with diabetes.