Electro-Optical Multi-Classification Platform for Minimizing Occasional Inaccuracy in Point-of-Care Biomarker Detection.

On-site diagnostic tests that accurately identify disease biomarkers lay the foundation for self-healthcare applications. However, these tests routinely rely on single-mode signals and suffer from insufficient accuracy, especially for multiplexed point-of-care tests within a few minutes. Here, we develop a dual-mode multi-classification diagnostic platform that integrates an electrochemiluminescence sensor and a field-effect transistor sensor in a microfluidic chip. The microfluidic channel guides the testing samples to flow across electro-optical sensor units, which produce dual-mode readouts by detecting infectious biomarkers of Mycobacterium tuberculosis, human rhinovirus, and group B streptococcus. Then, machine-learning classifiers generate three-dimensional hyperplanes to diagnose different diseases. Dual-mode readouts derived from distinct mechanisms enhance the anti-interference ability physically, and machine-learning-aided diagnosis in high-dimensional space reduces the occasional inaccuracy mathematically. Clinical validation studies with 501 unprocessed samples indicate that the platform has an accuracy approaching 99%, higher than the 77%∼93% accuracy of rapid point-of-care testing technologies at 100% statistical power (> 150 clinical tests). Moreover, the diagnosis time is 5 minutes without a trade-off of accuracy. This work solves the occasional inaccuracy issue of rapid on-site diagnosis, endowing POCT systems with the same accuracy as laboratory tests and holding unique prospects for complicated scenes of personalized healthcare. This article is protected by copyright. All rights reserved.