A Promising Approach: Artificial Intelligence Applied to Small Intestinal Bacterial Overgrowth (SIBO) Diagnosis Using Cluster Analysis.
A Promising Approach: Artificial Intelligence Applied to Small Intestinal Bacterial Overgrowth (SIBO) Diagnosis Using Cluster Analysis.
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一种有前景的方法：利用聚类分析将人工智能应用于小肠细菌过度生长 (SIBO) 诊断。
DOI：
10.3390/diagnostics11081445
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发表时间：
2021-08-10
期刊：
影响因子：
--
通讯作者：
Liu X
中科院分区：
文献类型：
--
作者：
Hao R;Zhang L;Liu J;Liu Y;Yi J;Liu X
小肠细菌过度生长(SIBO)是指肠道内细菌数量异常过多。由于症状和实验室检查是非特异性的，SIBO的诊断高度依赖于呼气测试。目前还缺乏一个普遍接受的呼吸测试诊断SIBO的分界点，而定义SIBO患者的两难境地增加了探索SIBO诊断的金标准的难度。如何在不定义SIBO患者的情况下验证呼气测试的黄金标准已成为临床上的迫切需求。收集湘雅医院近3年1071例患者的呼气试验数据，采用人工智能方法进行聚类分析。在用Hopkins统计量确定聚类倾向后，将K-Means算法和DBSCAN算法应用于数据集。用轮廓评分评价聚类效果的满意度，并描述每组的模式。从高维分析、数据驱动和区域特定饮食影响等方面探讨了人工智能在SIBO自适应呼气试验诊断标准中的应用优势。本研究为人工智能在SIBO诊断中的应用奠定了基础，为临床实践和科学研究提供了有益的参考。
Small intestinal bacterial overgrowth (SIBO) is characterized by abnormal and excessive amounts of bacteria in the small intestine. Since symptoms and lab tests are non-specific, the diagnosis of SIBO is highly dependent on breath testing. There is a lack of a universally accepted cut-off point for breath testing to diagnose SIBO, and the dilemma of defining “SIBO patients” has made it more difficult to explore the gold standard for SIBO diagnosis. How to validate the gold standard for breath testing without defining “SIBO patients” has become an imperious demand in clinic. Breath-testing datasets from 1071 patients were collected from Xiangya Hospital in the past 3 years and analyzed with an artificial intelligence method using cluster analysis. K-means and DBSCAN algorithms were applied to the dataset after the clustering tendency was confirmed with Hopkins Statistic. Satisfying the clustering effect was evaluated with a Silhouette score, and patterns of each group were described. Advantages of artificial intelligence application in adaptive breath-testing diagnosis criteria with SIBO were discussed from the aspects of high dimensional analysis, and data-driven and regional specific dietary influence. This research work implied a promising application of artificial intelligence for SIBO diagnosis, which would benefit clinical practice and scientific research.
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