# A Review of Electrochemical Sensors for the Detection of Glyated Hemoglobin
## 5 Conclusions and Future Prospects  
This review focuses on the different types of electrochemical HbA1c sensors developed in recent years, considering the working principles, construction methods, response results, clinical applications, strengths, and weaknesses. Regarding the detection range, the electrochemical HbA1c sensor can meet the detection demand of the percentage content of HbA1c, but it cannot directly measure the concentration in blood samples or serum, and samples need to be diluted or pretreated. From the detection target point of view, electrochemical HbA1c sensors can be divided into direct and indirect types. Direct type sensors use boric acid and its derivatives, ferrocene, antibodies, and other substances to detect HbA1c directly. Indirect methods detect the hydrolyzed product of HbA1c based on immobilized enzymes, including FAO, FPOX, and MIC. Advances in electrochemical technology have considerably reduced the complexity and cost of HbA1c detection. Electrochemical HbA1c sensors have a wide linear detection range and good repeatability and are suitable for the detection of HbA1c in clinical practice. Compared with commonly used glucose sensors, these sensors are more suitable for the long-term monitoring of blood glucose levels in diabetic patients. Electrochemical sensors have become an attractive alternative to conventional biosensing systems in the development of hospital or home diagnostic devices. The dynamic detection range of current HbA1c sensors is 4-20%, covering the range of human HbA1c levels. As clinical medicine techniques continue to be developed, there will be increased demand for diagnosis accuracy and efficiency. To better meet the needs for diabetes diagnosis, we believe that the repeatability and stability of electrochemical HbA1c sensors need to be improved to satisfy market demand for the future development of these sensors considering detection accuracy. This consideration is a major obstacle to translating such sensors into clinical applications and bringing them to market. Electrochemical HbA1c sensors have found broad applications in the medical field. With the advancement of artificial intelligence (AI) technology, we suggest that electrochemical sensors should be combined with AI to further facilitate the prevention of diabetes and daily physical examination. To date, many different machine learning techniques have been developed to predict and diagnose diabetes, such as support vector machines, random forests, logistic regression, and k-nearest neighbors. Cyclic voltammetry, alternating impedance, and square wave voltammetry can be used to obtain a large quantity of detection data. These data can then be preprocessed to extract features and build models for the accurate prediction of HbA1c and FV levels. AI can help improve the detection efficiency of electrochemical HbA1c sensors and reduce the manufacturing costs. A further goal should be to achieve immediate detection, the ability to analyze multiple components, and comprehensive health management for diabetic patients. Conceptualization, Y.Z. ; data curation, Y.L., Z.W., H.Z. and B.F.; formal analysis, Z.Z., Y.L., Z.W., H.Z. and Y.Z. ; funding acquisition, Z.Z. ; investigation, Z.Z., Y.L., Z.W., B.F., H.Z. and Y.Z. ; project administration, Z.Z., Y.Z. and W.J.L. ; supervision, Z.Z., H.Z., Y.Z. and W.J.L. ; visualization, Y.L., Z.W., H.Z. and B.F.; writing--original draft, Y.L. ; writing--review and editing, Z.Z., Y.L., H.Z., Y.Z. and W.J.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (Grant Nos. 61873307 and 61503322), the Natural Science Foundation of the Hebei Province of China (Grant No. F2021203070 and F2021501021), the Scientific Research Project of Colleges and Universities in Hebei Province (Grant No. ZD2019305), the Fundamental Research Funds for the Central Universities (Grant No. N2123004), the Qinhuangdao Science and Technology Planning Project (Grant No.