The consumption of microbial-contaminated food and water is responsible for the deaths of millions of people annually. Smartphone-based microscopy systems are portable, low-cost, and more accessible alternatives for the detection of Giardia and Cryptosporidium than traditional brightfield microscopes. However, the images from smartphone microscopes are noisier and require manual cyst identification by trained technicians, usually unavailable in resource-limited settings. Automatic detection of (oo)cysts using deep-learning-based object detection could offer a solution for this limitation. We evaluate the performance of three state-of-the-art object detectors to detect (oo)cysts of Giardia and Cryptosporidium on a custom dataset that includes both smartphone and brightfield microscopic images from vegetable samples. Faster RCNN, RetinaNet, and you only look once (YOLOv8s) deep-learning models were employed to explore their efficacy and limitations. Our results show that while the deep-learning models perform better with the brightfield microscopy image dataset than the smartphone microscopy image dataset, the smartphone microscopy predictions are still comparable to the prediction performance of non-experts.

Sickle cell disease (SCD) is a major public health priority throughout much of the world, affecting millions of people. In many regions, particularly those in resource-limited settings, SCD is not consistently diagnosed. In Africa, where the majority of SCD patients reside, more than 50% of the 0.2–0.3 million children born with SCD each year will die from it; many of these deaths are in fact preventable with correct diagnosis and treatment. Here, we present a deep learning framework which can perform automatic screening of sickle cells in blood smears using a smartphone microscope. This framework uses two distinct, complementary deep neural networks.

Microbiology might not be everyone's idea of fun, but a new DIY project from Stanford University aims to bring'playful interaction' to the study of single-celled organisms. A bioengineer has developed a 3-D printed smartphone microscope that allows users to observe microbes called Euglena, to make serious observations or even play games. The researchers have revealed interactive platforms to go along with the device, including a virtual soccer game and a Pac-Man-like maze, which rely on the movement of the tiny light-seeking organisms. A bioengineer has developed a 3-D printed smartphone microscope that allows users to observe microbes called Euglena, to make serious observations or even play games. A small platform on the device holds the microscope slide, where Euglena swim around freely. These microbes respond to light, and four LEDs activated by a joystick can be used to influence the directions by which they travel by varying the intensity.