A Multimode Microfiber Specklegram Biosensor for Measurement of CEACAM5 through AI Diagnosis

Author:

Liu Yuhui12ORCID,Lin Weihao13ORCID,Zhao Fang1ORCID,Liu Yibin1ORCID,Sun Junhui1,Hu Jie1ORCID,Li Jialong1,Chen Jinna1,Zhang Xuming2ORCID,Vai Mang I.3,Shum Perry Ping14ORCID,Shao Liyang14ORCID

Affiliation:

1. Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China

2. Department of Applied Physics, Hong Kong Polytechnic University, Hongkong 999077, China

3. Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China

4. Peng Cheng Laboratory, Shenzhen 518055, China

Abstract

Carcinoembryonic antigen (CEACAM5), as a broad-spectrum tumor biomarker, plays a crucial role in analyzing the therapeutic efficacy and progression of cancer. Herein, we propose a novel biosensor based on specklegrams of tapered multimode fiber (MMF) and two-dimensional convolutional neural networks (2D-CNNs) for the detection of CEACAM5. The microfiber is modified with CEA antibodies to specifically recognize antigens. The biosensor utilizes the interference effect of tapered MMF to generate highly sensitive specklegrams in response to different CEACAM5 concentrations. A zero mean normalized cross-correlation (ZNCC) function is explored to calculate the image matching degree of the specklegrams. Profiting from the extremely high detection limit of the speckle sensor, variations in the specklegrams of antibody concentrations from 1 to 1000 ng/mL are measured in the experiment. The surface sensitivity of the biosensor is 0.0012 (ng/mL)−1 within a range of 1 to 50 ng/mL. Moreover, a 2D-CNN was introduced to solve the problem of nonlinear detection surface sensitivity variation in a large dynamic range, and in the search for image features to improve evaluation accuracy, achieving more accurate CEACAM5 monitoring, with a maximum detection error of 0.358%. The proposed fiber specklegram biosensing scheme is easy to implement and has great potential in analyzing the postoperative condition of patients.

Funder

Department of Natural Resources of Guangdong Province

Science, Technology and Innovation Commission of Shenzhen Municipality

Intelligent Laser Basic Research Laboratory

Publisher

MDPI AG

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