Deep Learning‐Enhanced Hand‐Driven Microfluidic Chip for Multiplexed Nucleic Acid Detection Based on RPA/CRISPR - Tao Xu; Ying Zhang; Shunji Li; Chenxi Dai; Hongguo Wei; Dongjuan Chen; Yunjun Zhao; He Liu; Deliang Li; Peng Chen; Bi‐Feng Liu; Ye Tian (Advanced Science)
Abstract The early detection of high‐risk human papillomavirus (HR‐HPV) is crucial for the assessment and improvement of prognosis in cervical cancer. However, existing PCR‐based screening methods suffer from inadequate accessibility, which dampens the enthusiasm for screening among grassroots populations, especially in resource‐limited areas, and contributes to the persistently high mortality rate of cervical cancer. Here, a portable system is proposed for multiplexed nucleic acid detection, termed R‐CHIP, that integrates Recombinase polymerase amplification (RPA), CRISPR detection, Hand‐driven microfluidics, and an artificial Intelligence Platform. The system can go from sample pre‐processing to results readout in less than an hour with simple manual operation. Optimized for sensitivity of 10−17 M for HPV‐16 and 10−18 M for HPV‐18, R‐CHIP has an accuracy of over 95% in 300 tests on clinical samples. In addition, a smartphone microimaging system combined with the ResNet‐18 deep learning model is used to improve the readout efficiency and convenience of the detection system, with initial prediction accuracies of 96.0% and 98.0% for HPV‐16 and HPV‐18, respectively. R‐CHIP, as a user‐friendly and intelligent detection platform, has great potential for community‐level HR‐HPV screening in resource‐constrained settings, and contributes to the prevention and early diagnosis of other diseases.