AI-Based Portable White Blood Cells Classification and Counting System in POCT

As an important part of the human immune system, the changes in the number of white blood cells (WBCs) play a significant role in the diagnosis of diseases, the monitoring of treatment effects, and the evaluation of immune system function. Microscopic blood cell detection is the standard procedure for WBC classification and counting, but it is expensive and lacks repeatability due to the high cost of high-magnification microscopes and the need for expert medical support. In this article, a novel artificial intelligence (AI)-based portable leukocyte intelligent detection system is proposed. The system is intended to provide patients with more convenient and rapid WBC detection services at a lower cost and to provide an intuitive and effective reference for primary healthcare workers in diagnosing and treating diseases. The system simplifies the complex operations required by traditional counting methods, and all functions are highly integrated. Users only need to place a test card containing the blood sample to be tested into the card slot and turn on the device to complete the test easily. The hardware implementation of the system uses small-sized devices uniformly, and multiperspective sampling is achieved through the design of a motion mechanism to ensure the reliability of statistical results. To test the performance of the instrument, a new dataset was collected for testing, and an AI model more suitable for WBC classification purposes was obtained by improving the loss function. The results show that the detection model carried by the system has an error rate of less than 1% in WBC counting and an error rate of about 2% in classification, and the single detection time is less than 20 s. Overall, the system is portable, fast, easy to use, and has good scalability, which helps to automate and standardize disease diagnosis in point-of-care testing (POCT).