Department of Gastroenterology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China.
School of Life Sciences, University of Technology Sydney, Broadway, New South Wales, Australia.
J Gastroenterol Hepatol. 2021 Oct;36(10):2735-2744. doi: 10.1111/jgh.15530. Epub 2021 May 10.
Endoscopic screening for early detection of upper gastrointestinal (UGI) lesions is important. However, population-based endoscopic screening is difficult to implement in populous countries. By identifying high-risk individuals from the general population, the screening targets can be narrowed to individuals who are in most need of an endoscopy. This study was designed to develop an artificial intelligence (AI)-based model to predict patient risk of UGI lesions to identify high-risk individuals for endoscopy.
A total of 620 patients (from 5300 participants) were equally allocated into 10 parts for 10-fold cross validation experiments. The machine-learning predictive models for UGI lesion risk were constructed using random forest, logistic regression, decision tree, and support vector machine (SVM) algorithms. A total of 48 variables covering lifestyles, social-economic status, clinical symptoms, serological results, and pathological data were used in the model construction.
The accuracies of the four models were between 79.3% and 93.4% in the training set and between 77.2% and 91.2% in the testing dataset (logistics regression: 77.2%; decision tree: 87.3%; random forest: 88.2%; SVM: 91.2%;). The AUCs of four models showed impressive predictive ability. Comparing the four models with the different algorithms, the SVM model featured the best sensitivity and specificity in all datasets tested.
Machine-learning algorithms can accurately and reliably predict the risk of UGI lesions based on readily available parameters. The predictive models have the potential to be used clinically for identifying patients with high risk of UGI lesions and stratifying patients for necessary endoscopic screening.
内镜筛查有助于早期发现上消化道（UGI）病变。然而，在人口众多的国家，基于人群的内镜筛查难以实施。通过从普通人群中识别高危个体，可以将筛查目标缩小到最需要内镜检查的个体。本研究旨在开发一种基于人工智能（AI）的模型，以预测 UGI 病变患者的风险，从而识别需要进行内镜检查的高危个体。
将 620 名患者（来自 5300 名参与者）等分为 10 部分，用于 10 折交叉验证实验。使用随机森林、逻辑回归、决策树和支持向量机（SVM）算法构建 UGI 病变风险的机器学习预测模型。模型构建中使用了涵盖生活方式、社会经济状况、临床症状、血清学结果和病理数据的 48 个变量。
在训练集中，四个模型的准确率在 79.3%至 93.4%之间，在测试数据集中的准确率在 77.2%至 91.2%之间（逻辑回归：77.2%；决策树：87.3%；随机森林：88.2%；SVM：91.2%）。四个模型的 AUC 均显示出出色的预测能力。在比较不同算法的四个模型时，SVM 模型在所有测试数据集中均具有最佳的灵敏度和特异性。
机器学习算法可以基于易于获得的参数准确可靠地预测 UGI 病变的风险。这些预测模型有可能在临床上用于识别 UGI 病变风险较高的患者，并对患者进行内镜筛查的分层。
