Faculty of Industrial Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
Shaare Zedek Medical Center, Jerusalem, Israel.
Comput Methods Programs Biomed. 2022 Dec;227:107207. doi: 10.1016/j.cmpb.2022.107207. Epub 2022 Oct 31.
Recurrent attentive non-invasive observation of intestinal inflammation is essential for the proper management of Crohn's disease (CD). The goal of this study was to develop and evaluate a multi-modal machine-learning (ML) model to assess ileal CD endoscopic activity by integrating information from Magnetic Resonance Enterography (MRE) and biochemical biomarkers.
We obtained MRE, biochemical and ileocolonoscopy data from the multi-center ImageKids study database. We developed an optimized multimodal fusion ML model to non-invasively assess terminal ileum (TI) endoscopic disease activity in CD from MRE data. We determined the most informative features for model development using a permutation feature importance technique. We assessed model performance in comparison to the clinically recommended linear-regression MRE model in an experimental setup that consisted of stratified 2-fold validation, repeated 50 times, with the ileocolonoscopy-based Simple Endoscopic Score for CD at the TI (TI SES-CD) as a reference. We used the predictions' mean-squared-error (MSE) and the receiver operation characteristics (ROC) area under curve (AUC) for active disease classification (TI SEC-CD≥3) as performance metrics.
121 subjects out of the 240 subjects in the ImageKids study cohort had all required information (Non-active CD: 62 [51%], active CD: 59 [49%]). Length of disease segment and normalized biochemical biomarkers were the most informative features. The optimized fusion model performed better than the clinically recommended model determined by both a better median test MSE distribution (7.73 vs. 8.8, Wilcoxon test, p<1e-5) and a better aggregated AUC over the folds (0.84 vs. 0.8, DeLong's test, p<1e-9).
Optimized ML models for ileal CD endoscopic activity assessment have the potential to enable accurate and non-invasive attentive observation of intestinal inflammation in CD patients. The presented model is available at https://tcml-bme.github.io/ML_SESCD.html.
反复进行非侵入性的肠道炎症观察对于克罗恩病（CD）的恰当管理至关重要。本研究旨在开发和评估一种多模态机器学习（ML）模型，通过整合磁共振肠造影术（MRE）和生化生物标志物的信息来评估回肠 CD 的内镜活动。
我们从多中心的 ImageKids 研究数据库中获得了 MRE、生化和回结肠镜数据。我们开发了一种优化的多模态融合 ML 模型，通过 MRE 数据来非侵入性地评估 CD 患者的末端回肠（TI）内镜疾病活动。我们使用排列特征重要性技术来确定最具信息量的特征以用于模型开发。我们在一个实验设置中评估了模型性能，该设置由分层 2 倍验证组成，重复了 50 次，以 TI 上基于回结肠镜的简单 CD 内镜评分（TI SES-CD）为参考。我们使用预测的均方误差（MSE）和用于主动疾病分类（TI SEC-CD≥3）的接收器操作特性（ROC）曲线下面积（AUC）作为性能指标。
在 ImageKids 研究队列的 240 名受试者中，有 121 名受试者具有所有必需信息（非活动 CD：62 [51%]，活动 CD：59 [49%]）。疾病段长度和归一化生化标志物是最具信息量的特征。优化融合模型的表现优于临床推荐模型，这表现在更好的中位数测试 MSE 分布（7.73 与 8.8，Wilcoxon 检验，p<1e-5）和更好的折叠聚合 AUC（0.84 与 0.8，DeLong 检验，p<1e-9）。
用于回肠 CD 内镜活动评估的优化 ML 模型有可能实现对 CD 患者肠道炎症的准确和非侵入性的密切观察。该模型可在 https://tcml-bme.github.io/ML_SESCD.html 上获得。
