A new dynamic deep learning noise elimination method for chip-based real-time PCR

Abstract

Point-of-care (POC) real-time polymerase chain reaction (PCR) has become one of the most important technologies for many fields such as pathogen detection and water-quality monitoring. POC real-time PCR usually adopts chips with small-volume chambers for portability, which is more likely to produce complex noise that seriously affects the accuracy. Such complex noises are difficult to be eliminated by the traditional fixed area algorithm that is most commonly used at present because they usually have random shape, location, and brightness. To address this problem, we proposed a novel image analysis method, Dynamic Deep Learning Noise Elimination Method (DIPLOID), in this paper. Our new method could recognize and output the mask of the interference by Mask R-CNN, and then subtract the interference and select the maximum valid contiguous area for brightness analysis by dynamic programming. Compared with the traditional method, DIPLOID increased the accuracy, sensitivity, and specificity from 57.9 to 94.6%, 49.1 to 93.9%, and 65.9 to 95.2%, respectively. DIPLOID has great anti-interference, robustness, and sensitivity, which can reduce the impact of complex noise as much as possible from the aspect of the algorithm. As shown in the experiments of this paper, our method significantly improved the accuracy to over 94% under the complex noise situation, which could make the POC real-time PCR have greater potential in the future.