https://www.sciencedaily.com/releases/2023/05/230502155410.htm

Holger Schmidt, distinguished professor of electrical and computer engineering at UC Santa Cruz, and his group have long been focused on developing unique, highly sensitive devices called optofluidic chips to detect biomarkers.

Schmidt's graduate student Vahid Ganjalizadeh led an effort to use machine learning to enhance their systems by improving its ability to accurately classify biomarkers. The deep neural network he developed classifies particle signals with 99.8 percent accuracy in real time, on a system that is relatively cheap and portable for point-of-care applications, as shown in a new paper in Nature Scientific Reports.