Diagnostic test that combines two technologies with machine learning could lead to new paradigm for at-home testing

A new diagnostic test system jointly developed at the University of Chicago Pritzker School of Molecular Engineering (PME) and UCLA Samueli School of Engineering fuses a powerful, sensitive transistor with a cheap, paper-based diagnostic test. When combined with machine learning, the system becomes a new kind of biosensor that could ultimately transform at-home testing and diagnostics.