Non‐invasive screening of bladder cancer using digital microfluidics and FLIM technology combined with deep learning

Author:

Su Wenhua1,Xu Chenyang2,Hu Jinzhong2,Chen Qiushu1,Yang Yuwei1,Ji Mingmei1,Fei Yiyan1,Ma Jiong134ORCID,Jiang Haowen2,Mi Lan13ORCID

Affiliation:

1. Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra‐Precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), School of Information Science and Technology Fudan University Shanghai China

2. Department of Urology, Huashan Hospital Fudan University Shanghai China

3. Institute of Biomedical Engineering and Technology, Academy for Engineer and Technology Fudan University Shanghai China

4. Shanghai Engineering Research Center of Industrial Microorganisms, The Multiscale Research Institute of Complex Systems (MRICS), School of Life Sciences Fudan University Shanghai China

Abstract

AbstractNon‐invasive screening for bladder cancer is crucial for treatment and postoperative follow‐up. This study combines digital microfluidics (DMF) technology with fluorescence lifetime imaging microscopy (FLIM) for urine analysis and introduces a novel non‐invasive bladder cancer screening technique. Initially, the DMF was utilized to perform preliminary screening and enrichment of urine exfoliated cells from 54 participants, followed by cell staining and FLIM analysis to assess the viscosity of the intracellular microenvironment. Subsequently, a deep learning residual convolutional neural network was employed to automatically classify FLIM images, achieving a three‐class prediction of high‐risk (malignant), low‐risk (benign), and minimal risk (normal) categories. The results demonstrated a high consistency with pathological diagnosis, with an accuracy of 91% and a precision of 93%. Notably, the method is sensitive for both high‐grade and low‐grade bladder cancer cases. This highly accurate non‐invasive screening method presents a promising approach for bladder cancer screening with significant clinical application potential.

Funder

National Natural Science Foundation of China

Shanghai Municipal Hospital Development Center

Science and Technology Commission of Shanghai Municipality

Publisher

Wiley

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