Periodontitis is closely related to many systemic diseases linked by different periodontal pathogens. To unravel the relationship between periodontitis and systemic diseases, it is very important to correctly discriminate major periodontal pathogens. To realize convenient, efficient, and high-accuracy bacterial species classification, the authors use Raman spectroscopy combined with machine learning algorithms to distinguish three major periodontal pathogens Porphyromonas gingivalis (Pg), Fusobacterium nucleatum (Fn), and Aggregatibacter actinomycetemcomitans (Aa). The result shows that this novel method can successfully discriminate the three above-mentioned periodontal pathogens. Moreover, the classification accuracies for the three categories of the original data were 94.7% at the sample level and 93.9% at the spectrum level by the machine learning algorithm extra trees. This study provides a fast, simple, and accurate method which is very beneficial to differentiate periodontal pathogens.

中文翻译：

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使用拉曼光谱结合机器学习算法鉴别牙周病原菌

牙周炎与许多由不同牙周病原体相关的全身性疾病密切相关。为了阐明牙周炎与全身性疾病之间的关系，正确区分主要牙周病原菌非常重要。为实现方便、高效、高精度的菌种分类，作者利用拉曼光谱结合机器学习算法，对三种主要牙周病原菌进行区分牙龈卟啉单胞菌(PG),具核梭杆菌(Fn）， 和伴放线菌聚集杆菌(氨基酸）。结果表明，这种新方法可以成功区分上述三种牙周病原菌。此外，机器学习算法额外树对原始数据三类的分类准确率在样本级别为94.7%，在频谱级别为93.9%。本研究提供了一种快速、简便、准确的方法，对鉴别牙周病原菌非常有益。