Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, 14th Floor, Chicago, IL 60611, USA.
Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, 14th Floor, Chicago, IL 60611, USA.
Artif Intell Med. 2022 Feb;124:102233. doi: 10.1016/j.artmed.2021.102233. Epub 2021 Dec 25.
High-resolution manometry (HRM) is the primary procedure used to diagnose esophageal motility disorders. Its manual interpretation and classification, including evaluation of swallow-level outcomes and then derivation of a study-level diagnosis based on Chicago Classification (CC), may be limited by inter-rater variability and inaccuracy of an individual interpreter. We hypothesized that an automatic diagnosis platform using machine learning and artificial intelligence approaches could be developed to accurately identify esophageal motility diagnoses. Further, a multi-stage modeling framework, akin to the step-wise approach of the CC, was utilized to leverage advantages of a combination of machine learning approaches including deep-learning models and feature-based models. Models were trained and tested using a dataset comprised of 1741 patients' HRM studies with CC diagnoses assigned by expert physician raters. In the swallow-level stage, three models based on convolutional neural networks (CNNs) were developed to predict swallow type and swallow pressurization (test accuracies of 0.88 and 0.93, respectively), and integrated relaxation pressure (IRP)(regression model with test error of 4.49 mmHg). At the study-level stage, model selection from families of the expert-knowledge-based rule models, xgboost models and artificial neural network(ANN) models were conducted. A simple model-agnostic strategy of model balancing motivated by Bayesian principles was utilized, which gave rise to model averaging weighted by precision scores. The averaged (blended) models and individual models were compared and evaluated, of which the best performance on test dataset is 0.81 in top-1 prediction, 0.92 in top-2 predictions. This is the first artificial-intelligence style model to automatically predict esophageal motility (CC) diagnoses from HRM studies using raw multi-swallow data and it achieved high accuracy. Thus, this proposed modeling framework could be broadly applied to assist with HRM interpretation in a clinical setting.
高分辨率测压（HRM）是诊断食管动力障碍的主要方法。其手动解释和分类，包括对吞咽水平结果的评估，然后根据芝加哥分类（CC）得出研究水平的诊断，可能受到评分者间变异性和个体解释者的不准确性的限制。我们假设可以开发一种使用机器学习和人工智能方法的自动诊断平台，以准确识别食管动力诊断。此外，使用类似于 CC 的逐步方法的多阶段建模框架，利用了包括深度学习模型和基于特征的模型在内的多种机器学习方法的优势。使用由 1741 名患者 HRM 研究组成的数据集，这些研究由专家医师评分者分配 CC 诊断，对模型进行了训练和测试。在吞咽水平阶段，开发了三个基于卷积神经网络（CNN）的模型，以预测吞咽类型和吞咽加压（测试准确率分别为 0.88 和 0.93）和整合松弛压力（IRP）（测试误差为 4.49mmHg 的回归模型）。在研究水平阶段，对基于专家知识规则模型、xgboost 模型和人工神经网络（ANN）模型的模型家族进行了选择。利用基于贝叶斯原理的简单无模型策略来进行模型平衡，从而产生由精度得分加权的模型平均。比较和评估了平均（混合）模型和单个模型，其中测试数据集上的最佳性能是 0.81 的首位预测，0.92 的前两位预测。这是第一个使用原始多吞咽数据从 HRM 研究中自动预测食管动力（CC）诊断的人工智能风格模型，它实现了高精度。因此，该建模框架可广泛应用于协助临床 HRM 解释。
