A Multimode Microfiber Specklegram Biosensor for Measurement of CEACAM5 through AI Diagnosis.
Yuhui LiuWeihao LinFang ZhaoYibin LiuJunhui SunJie HuJialong LiJinna ChenXuming ZhangMang I VaiPerry Ping ShumLiyang ShaoPublished in: Biosensors (2024)
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.
Keyphrases
- label free
- convolutional neural network
- deep learning
- gold nanoparticles
- loop mediated isothermal amplification
- real time pcr
- end stage renal disease
- newly diagnosed
- squamous cell carcinoma
- artificial intelligence
- chronic kidney disease
- patients undergoing
- prognostic factors
- papillary thyroid
- peritoneal dialysis
- climate change
- human health
- risk assessment
- patient reported