Towards an interpretable classifier for characterization of endoscopic Mayo scores in ulcerative colitis using Raman Spectroscopy.,Analytical Chemistry

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Towards an interpretable classifier for characterization of endoscopic Mayo scores in ulcerative colitis using Raman Spectroscopy.
Analytical Chemistry ( IF 6.7 ) Pub Date : 2020-09-23 , DOI: 10.1021/acs.analchem.0c02163
Tatiana Kirchberger-Tolstik _{1,

2} , Pranita Pradhan _{1,

3} , Michael Vieth ₄ , Philip Grunert ₂ , Juergen Popp _{1,

3} , Thomas Wilhelm Bocklitz _{1,

3} , Andreas Stallmach ₂

Affiliation

Ulcerative colitis (UC) is one of the main types of chronic inflammatory diseases that affect the bowel, but its pathogenesis is yet to be completely defined. Assessing the disease activity of UC is vital for developing a personalized treatment. Conventionally, the assessment of UC is performed by colonoscopy and histopathology. However, conventional methods fail to retain biomolecular information associated to the severity of UC and are solely based on morphological characteristics of the inflamed colon. Furthermore, assessing endoscopic disease severity is limited by the requirement for experienced human reviewers. Therefore, this work presents a nondestructive biospectroscopic technique, for example, Raman spectroscopy, for assessing endoscopic disease severity according to the four-level Mayo subscore. This contribution utilizes multidimensional Raman spectroscopic data to generate a predictive model for identifying colonic inflammation. The predictive modeling of the Raman spectroscopic data is performed using a one-dimensional deep convolutional neural network (1D-CNN). The classification results of 1D-CNN achieved a mean sensitivity of 78% and a mean specificity of 93% for the four Mayo endoscopic scores. Furthermore, the results of the 1D-CNN are interpreted by a first-order Taylor expansion, which extracts the Raman bands important for classification. Additionally, a regression model of the 1D-CNN model is constructed to study the extent of misclassification and border-line patients. The overall results of Raman spectroscopy with 1D-CNN as a classification and regression model show a good performance, and such a method can serve as a complementary method for UC analysis.

中文翻译：

迈向使用拉曼光谱法表征溃疡性结肠炎内镜Mayo评分的可解释分类器。

溃疡性结肠炎（UC）是影响肠道的主要慢性炎症性疾病之一，但其发病机理尚未完全阐明。评估UC的疾病活性对于开发个性化治疗至关重要。通常，通过结肠镜检查和组织病理学进行UC的评估。但是，常规方法无法保留与UC严重程度相关的生物分子信息，并且仅基于发炎结肠的形态特征。此外，评估内窥镜疾病的严重程度受到有经验的人类检查员的要求的限制。因此，这项工作提出了一种非破坏性生物光谱技术，例如拉曼光谱法，用于根据四级Mayo评分来评估内窥镜疾病的严重程度。该贡献利用多维拉曼光谱数据来生成用于识别结肠炎症的预测模型。拉曼光谱数据的预测模型是使用一维深度卷积神经网络（1D-CNN）进行的。1D-CNN的分类结果对四个Mayo内窥镜评分的平均敏感性为78％，平均特异性为93％。此外，一维-CNN的结果由一阶泰勒展开解释，该展开提取了对分类重要的拉曼谱带。此外，还构建了1D-CNN模型的回归模型来研究误分类和边界患者的程度。以1D-CNN作为分类和回归模型的拉曼光谱的整体结果显示出良好的性能，

更新日期：2020-10-21

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