Electro‐Optical Multiclassification Platform for Minimizing Occasional Inaccuracy in Point‐of‐Care Biomarker Detection

Abstract

Abstract 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 (POCTs) within a few minutes. Here, this work develops a dual‐mode multiclassification 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 tuberculosis (TB), human rhinovirus (HRV), and group B streptococcus (GBS). Then, machine‐learning classifiers generate three‐dimensional (3D) 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 min 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.