The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.

医疗保健行业迫切需要快速微生物鉴定技术来治疗微生物感染。微生物感染是世界范围内的一个主要医疗保健问题，因为这些广泛传播的疾病往往会发展成致命的症状。虽然研究表明早期适当的抗生素治疗可以显着降低感染的死亡率，但这种有效的治疗方法很难实施。早期适当抗生素治疗的主要障碍是常规微生物鉴定的周转时间长，其中包括耗时的样品生长。在这里，我们提出了一个基于显微镜的框架，可以从单个细胞到几个细胞中识别病原体。我们的框架通过结合三维定量相位成像和人工神经网络来获取并利用有限样本的形态。我们展示了对导致血流感染的 19 种细菌的鉴定，单个细菌细胞或细菌簇的准确率达到 82.5%。这种性能可与足够量样品下的金标准质谱相媲美，支撑了我们的框架在临床应用中的有效性。此外，我们的准确度随着多次测量的增加而增加，对细胞或簇的七种不同测量达到 99.9%。我们相信，我们的框架可以在感染的初始治疗期间为临床医生提供有益的咨询工具。