Chetcuti Zammit Stefania, Bull Lawrence A, Sanders David S, Galvin Jessica, Dervilis Nikolaos, Sidhu Reena, Worden Keith
Academic Unit, Department of Gastroenterology, Sheffield Teaching Hospitals, Sheffield, UK.
Gastroenterology Department, Royal Hallamshire Hospital, Glossop Road, Sheffield, S102JF, UK.
J Med Syst. 2020 Oct 2;44(11):195. doi: 10.1007/s10916-020-01657-9.
Small bowel capsule endoscopy (SBCE) can be complementary to histological assessment of celiac disease (CD) and serology negative villous atrophy (SNVA). Determining the severity of disease on SBCE using statistical machine learning methods can be useful in the follow up of patients. SBCE can play an additional role in differentiating between CD and SNVA. De-identified SBCEs of patients with CD and SNVA were included. Probabilistic analysis of features on SBCE were used to predict severity of duodenal histology and to distinguish between CD and SNVA. Patients with higher Marsh scores were more likely to have a positive SBCE and a continuous distribution of macroscopic features of disease than those with lower Marsh scores. The same pattern was also true for patients with CD when compared to patients with SNVA. The validation accuracy when predicting the severity of Marsh scores and when distinguishing between CD and SNVA was 69.1% in both cases. When the proportions of each SBCE class group within the dataset were included in the classification model, to distinguish between the two pathologies, the validation accuracy increased to 75.3%. The findings of this work suggest that by using features of CD and SNVA on SBCE, predictions can be made of the type of pathology and the severity of disease.
小肠胶囊内镜检查（SBCE）可作为乳糜泻（CD）和血清学阴性绒毛萎缩（SNVA）组织学评估的补充。使用统计机器学习方法确定SBCE上疾病的严重程度对患者随访可能有用。SBCE在区分CD和SNVA方面可发挥额外作用。纳入了CD和SNVA患者的去识别化SBCE。利用SBCE上特征的概率分析来预测十二指肠组织学的严重程度，并区分CD和SNVA。与Marsh评分较低的患者相比，Marsh评分较高的患者更有可能出现阳性SBCE和疾病宏观特征的连续分布。与SNVA患者相比，CD患者也呈现相同模式。预测Marsh评分严重程度以及区分CD和SNVA时的验证准确率在两种情况下均为69.1%。当将数据集中每个SBCE类别组的比例纳入分类模型以区分这两种病理情况时，验证准确率提高到75.3%。这项工作的结果表明，通过利用SBCE上CD和SNVA的特征，可以对病理类型和疾病严重程度进行预测。
