A group of scientists led by researchers from the University of Tokyo have developed an automated, high-throughput system that relies on imaging droplets of biofluids (such as blood, saliva and urine) for disease diagnosis in an attempt to reduce the number of consumables and equipment needed for biomedical testing.

The work is published in the journal Advanced Intelligent Systems.

In the workflow, biofluid droplet images are analyzed by machine-learning algorithms to diagnose disease. Remarkably, the technology relies on the drying process of biofluid droplets to distinguish between normal and abnormal samples.