标题： 使用数字无透镜全息术对腹膜透析液中的颗粒物和细菌进行实时分析 显示英文标题

标题： Realtime Particulate Matter and Bacteria Analysis of Peritoneal Dialysis Fluid using Digital Inline Holography

摘要： 我们开发了一种集成深度学习算法的数字全息术（DIH）系统，用于实时检测腹膜透析（PD）液体中的颗粒物（PM）和细菌污染。 该系统包括一个微流体样品输送模块和一个DIH成像模块，该模块使用脉冲激光器和带有40倍物镜的数码相机捕捉全息图。 我们的数据处理流程增强全息图，重建图像，并采用基于YOLOv8n的深度学习模型进行粒子识别和分类，该模型在标记的通用PD粒子、大肠杆菌（E. coli）和铜绿假单胞菌（P. aeruginosa）的全息图上进行训练。 该系统有效检测和分类了无菌PD液体中的通用粒子，显示出主要尺寸为1-5微米的多种形态，平均浓度为每微升61个粒子。 在掺入高浓度大肠杆菌和铜绿假单胞菌的PD液体样本中，我们的系统在临床相关的低假阳性率下实现了对这些细菌的高灵敏度检测和分类。 通过使用掺入约100至10,000个细菌/毫升细菌浓度的PD液体，与标准菌落形成单位（CFU）方法进行进一步验证，显示了我们的测量结果与CFU计数之间明确的一一对应关系。 我们的DIH系统为评估PD液体中的细菌污染提供了一种快速、准确的传统培养方法的替代方案。 通过实现实时无菌监测，它可以显著改善PD治疗的患者预后，促进现场液体生产，减少物流挑战，并可扩展到制药生产中的质量控制。 显示英文摘要

摘要： We developed a digital inline holography (DIH) system integrated with deep learning algorithms for real-time detection of particulate matter (PM) and bacterial contamination in peritoneal dialysis (PD) fluids. The system comprises a microfluidic sample delivery module and a DIH imaging module that captures holograms using a pulsed laser and a digital camera with a 40x objective. Our data processing pipeline enhances holograms, reconstructs images, and employs a YOLOv8n-based deep learning model for particle identification and classification, trained on labeled holograms of generic PD particles, Escherichia coli (E. coli), and Pseudomonas aeruginosa (P. aeruginosa). The system effectively detected and classified generic particles in sterile PD fluids, revealing diverse morphologies predominantly sized 1-5 um with an average concentration of 61 particles per microliter. In PD fluid samples spiked with high concentrations of E. coli and P. aeruginosa, our system achieved high sensitivity in detecting and classifying these bacteria at clinically relevant low false positive rates. Further validation against standard colony-forming unit (CFU) methods using PD fluid spiked with bacterial concentrations from approximately 100 to 10,000 bacteria per milliliter demonstrated a clear one-to-one correspondence between our measurements and CFU counts. Our DIH system provides a rapid, accurate alternative to traditional culture-based methods for assessing bacterial contamination in PD fluids. By enabling real-time sterility monitoring, it can significantly improve patient outcomes in PD treatment, facilitate point-of-care fluid production, reduce logistical challenges, and be extended to quality control in pharmaceutical production.