Machine vision algorithms are increasingly utilized in the detection and analysis of blood samples on dried blood spot (DBS) cards due to their precision, speed, and ability to handle large volumes of data. These algorithms often employ image processing techniques such as thresholding, edge detection, and segmentation to identify and isolate blood spots from the background of the card. Advanced methods like machine learning and deep learning are also being used to enhance accuracy by training models on diverse datasets to recognize various patterns and anomalies in blood stains. Convolutional neural networks (CNNs), in particular, have shown great promise in automatically classifying the quality and quantity of blood samples based on texture, color, and size. By integrating these algorithms into automated systems, laboratories can achieve more consistent and reliable assessments of blood samples, improving the efficiency of screening processes and minimizing human error.