PDF(Pubmed)
Abstract：
Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h.
摘要：
将人工智能和新的诊断平台集成到常规的临床微生物学实验室程序中变得越来越有趣。持有减少周转时间和成本并最大限度地提高效率的承诺。至少有10亿人患有真菌感染，每年导致超过160万人死亡。尽管对真菌诊断的需求不断增加，当前的方法受到人工偏见的影响，长栽培时间（从几天到几个月），和低敏感性（只有50％产生阳性真菌培养）。因此，延迟和不准确的治疗导致更高的医院费用，流动性和死亡率。这里,我们开发了单细胞拉曼光谱和人工智能，以实现对传染性真菌的快速鉴定。最初使用单细胞拉曼光谱（SCRS）以100％的灵敏度和特异性实现了真菌和细菌感染之间的分类。然后，我们从94名患者的临床真菌分离株中构建了一个拉曼数据集，由115,129SCRS组成。通过使用优化的临床反馈循环训练分类模型,每个患者只有5个细胞(每个细胞采集时间2秒)进行了最准确的分类。该方案在物种水平上实现了真菌鉴定的100％准确性。该方案被转化为评估尿路感染的临床样本，在1小时内从原始样本到结果获得正确的诊断。
