Work

Deep Learning‐Enabled Multiplexed Point‐of‐Care Sensor using a Paper‐Based Fluorescence Vertical Flow Assay (Small 51/2023)

Year: 2023

Type: article

Abstract: Cardiac Biomarkers In article number 2300617, Aydogan Ozcan and co-workers demonstrate a deep learning-enabled multiplexed point-of-care sensor using a paper-based fluorescence vertical flow assay to simultaneously quantify three cardiac biomarkers in serum. Using a low-cost mobile reader, this multiplexed sensor quantifies the target biomarkers through trained neural networks, all within <15 min of test time using 50 μL of serum sample per patient.

Source: Small

DOI: 10.1002/smll.202370431

Authors: Artem Goncharov, Hyou‐Arm Joung, Rajesh Ghosh, Gyeo‐Re Han, Zachary S. Ballard, Quinn Maloney, Alexandra Bell, Chew Tin Zar Aung, Omai B. Garner, Dino Di Carlo, Aydogan Ozcan

Institutions: University of California, Los Angeles, California NanoSystems Institute

Language: English

Topic: Biosensors and Analytical Detection

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

OpenAlex ID: W4389994078

FWCI: 0.236

Cites:

Cited by: 3

Related to: 10