Issue 23, 2022

Deep learning-assisted sensitive detection of fentanyl using a bubbling-microchip

Abstract

Deep learning-enabled smartphone-based image processing has significant advantages in the development of point-of-care diagnostics. Conventionally, most deep-learning applications require task specific large scale expertly annotated datasets. Therefore, these algorithms are oftentimes limited only to applications that have large retrospective datasets available for network development. Here, we report the possibility of utilizing adversarial neural networks to overcome this challenge by expanding the utility of non-specific data for the development of deep learning models. As a clinical model, we report the detection of fentanyl, a small molecular weight drug that is a type of opioid, at the point-of-care using a deep-learning empowered smartphone assay. We used the catalytic property of platinum nanoparticles (PtNPs) in a smartphone-enabled microchip bubbling assay to achieve high analytical sensitivity (detecting fentanyl at concentrations as low as 0.23 ng mL−1 in phosphate buffered saline (PBS), 0.43 ng mL−1 in human serum and 0.64 ng mL−1 in artificial human urine). Image-based inferences were made by our adversarial-based SPyDERMAN network that was developed using a limited dataset of 104 smartphone images of microchips with bubble signals from tests performed with known fentanyl concentrations and using our retrospective library of 17 573 non-specific bubbling-microchip images. The accuracy (± standard error of mean) of the developed system in determining the presence of fentanyl, when using a cutoff concentration of 1 ng mL−1, was 93 ± 0% in human serum (n = 100) and 95.3 ± 1.5% in artificial human urine (n = 100).

Graphical abstract: Deep learning-assisted sensitive detection of fentanyl using a bubbling-microchip

Supplementary files

Article information

Article type
Paper
Submitted
25 mai 2022
Accepted
21 oct. 2022
First published
31 oct. 2022

Lab Chip, 2022,22, 4531-4540

Deep learning-assisted sensitive detection of fentanyl using a bubbling-microchip

H. Chen, S. Kim, J. M. Hardie, P. Thirumalaraju, S. Gharpure, S. Rostamian, S. Udayakumar, Q. Lei, G. Cho, M. K. Kanakasabapathy and H. Shafiee, Lab Chip, 2022, 22, 4531 DOI: 10.1039/D2LC00478J

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