Core Viewpoint - The collaboration between Fudan University and Shanghai Xiaohai Turtle Technology Co., Ltd. has led to the development of R³Net, a robust quantitative method based on a three-phase neural network for processing noisy cdPCR images [1][2]. Group 1: Technology Overview - R³Net employs a three-stage processing workflow: noise recognition, image restoration, and chip reading. It utilizes a U-Net network for precise noise area identification and generates noise masks, followed by an innovative S-SRNet for image restoration, and finally a lightweight YOLO-mini network for high-precision quantitative analysis [3]. - The unique aspect of this technology is its temporal input mechanism, which allows the network to effectively distinguish between foreground noise and background areas by inputting noisy images and noise masks at different time steps [3]. - The introduction of Spiking Neural Networks (SNN) enhances computational efficiency and temporal sensitivity, providing a new solution for image processing in complex noise environments [3]. Group 2: Performance Metrics - Extensive testing on DNA samples from lung cancer, COVID-19, and influenza viruses shows that R³Net outperforms traditional methods in key metrics: it achieves an accuracy rate of 88.47% when processing interfered images, with clarity and similarity indices of 41.38 and 99.72, respectively [4]. - The lightweight algorithm maintains a high precision of 98.27% while reducing system resource usage by over 98% compared to traditional methods, processing an image in just 1.3 seconds, thus meeting the demands for rapid clinical testing [4]. - The application of R³Net technology signifies that reliable detection results can be obtained even in complex experimental environments, marking the entry of digital PCR into an AI-driven era [4].