Molder Adriana, Balaban Daniel Vasile, Molder Cristian-Constantin, Jinga Mariana, Robin Antonin
Center of Excellence in Robotics and Autonomous Systems, Military Technical Academy Ferdinand I, 050141 Bucharest, Romania.
Internal Medicine and Gastroenterology, Central Military Emergency University Hospital, Carol Davila University of Medicine and Pharmacy, 030167 Bucharest, Romania.
Diagnostics (Basel). 2023 Aug 28;13(17):2780. doi: 10.3390/diagnostics13172780.
Celiac disease (CD) is a lifelong chronic autoimmune systemic disease that primarily affects the small bowel of genetically susceptible individuals. The diagnostics of adult CD currently rely on specific serology and the histological assessment of duodenal mucosa on samples taken by upper digestive endoscopy. Because of several pitfalls associated with duodenal biopsy sampling and histopathology, and considering the pediatric no-biopsy diagnostic criteria, a biopsy-avoiding strategy has been proposed for adult CD diagnosis also. Several endoscopic changes have been reported in the duodenum of CD patients, as markers of villous atrophy (VA), with good correlation with serology. In this setting, an opportunity lies in the automated detection of these endoscopic markers, during routine endoscopy examinations, as potential case-finding of unsuspected CD. We collected duodenal endoscopy images from 18 CD newly diagnosed CD patients and 16 non-CD controls and applied machine learning (ML) and deep learning (DL) algorithms on image patches for the detection of VA. Using histology as standard, high diagnostic accuracy was seen for all algorithms tested, with the layered convolutional neural network (CNN) having the best performance, with 99.67% sensitivity and 98.07% positive predictive value. In this pilot study, we provide an accurate algorithm for automated detection of mucosal changes associated with VA in CD patients, compared to normally appearing non-atrophic mucosa in non-CD controls, using histology as a reference.
乳糜泻（CD）是一种终身慢性自身免疫性全身性疾病，主要影响具有遗传易感性个体的小肠。目前，成人CD的诊断依赖于特定的血清学检查以及对上消化道内镜采集样本的十二指肠黏膜进行组织学评估。由于十二指肠活检采样和组织病理学存在一些缺陷，并考虑到儿科无活检诊断标准，因此也有人提出了一种避免活检的策略用于成人CD的诊断。已有报道称，CD患者十二指肠出现了几种内镜下改变，作为绒毛萎缩（VA）的标志物，与血清学检查具有良好的相关性。在这种情况下，在常规内镜检查过程中自动检测这些内镜标志物，有可能发现未被怀疑的CD病例。我们收集了18例新诊断的CD患者和16例非CD对照的十二指肠内镜图像，并将机器学习（ML）和深度学习（DL）算法应用于图像块以检测VA。以组织学为标准，所有测试算法均具有较高的诊断准确性，其中分层卷积神经网络（CNN）性能最佳，灵敏度为99.67%，阳性预测值为98.07%。在这项初步研究中，我们提供了一种准确的算法，以组织学为参考，与非CD对照中正常外观的非萎缩性黏膜相比，自动检测CD患者中与VA相关的黏膜变化。
