PDF(Pubmed)
Abstract：
In recent decades, preterm birth (PTB) has become a significant research focus in the healthcare field, as it is a leading cause of neonatal mortality worldwide. Using five independent study cohorts including 1290 vaginal samples from 561 pregnant women who delivered at term (n = 1029) or prematurely (n = 261), we analysed vaginal metagenomics data for precise microbiome structure characterization. Then, a deep neural network (DNN) was trained to predict term birth (TB) and PTB with an accuracy of 84.10% and an area under the receiver operating characteristic curve (AUROC) of 0.875 ± 0.11. During a benchmarking process, we demonstrated that our DL model outperformed seven currently used machine learning algorithms. Finally, our results indicate that overall diversity of the vaginal microbiota should be taken in account to predict PTB and not specific species. This artificial-intelligence based strategy should be highly helpful for clinicians in predicting preterm birth risk, allowing personalized assistance to address various health issues. DeepMPTB is open source and free for academic use. It is licensed under a GNU Affero General Public License 3.0 and is available at https://deepmptb.streamlit.app/ . Source code is available at https://github.com/oschakoory/DeepMPTB and can be easily installed using Docker ( https://www.docker.com/ ).
摘要：
近几十年来,早产（PTB）已成为医疗保健领域的重要研究热点，因为它是全球新生儿死亡的主要原因。使用五个独立的研究队列，包括来自561名孕妇的1290个阴道样本，这些孕妇在足月分娩（n=1029）或过早分娩（n=261），我们分析了阴道宏基因组学数据,以获得精确的微生物组结构表征.然后，训练了深度神经网络(DNN)来预测足月出生(TB)和PTB,准确率为84.10%,受试者工作特征曲线下面积(AUROC)为0.875±0.11.在基准测试过程中，我们证明了我们的DL模型优于目前使用的7种机器学习算法.最后,我们的结果表明,在预测PTB时,应考虑阴道微生物群的总体多样性,而非特定物种.这种基于人工智能的策略应该对临床医生预测早产风险非常有帮助。允许个性化援助来解决各种健康问题。DeepMPTB是开源的，免费供学术使用。它根据GNUAffero通用公共许可证3.0获得许可，可在https://deepmptb上获得。流光。app/.源代码可在https://github.com/oschakoory/DeepMPTB上获得，可以使用Docker轻松安装（https://www。docker.com/)。
