Furlanello Cesare, Bussola Nicole, Merzi Nicolò, Pievani Trapletti Giovanni, Cadei Moris, Del Sordo Rachele, Sidoni Angelo, Ricci Chiara, Lanzarotto Francesco, Parigi Tommaso Lorenzo, Villanacci Vincenzo
Orobix Life, Bergamo, Italy; LIGHT Center, Brescia, Italy.
Orobix Life, Bergamo, Italy.
Dig Liver Dis. 2025 Jan;57(1):184-189. doi: 10.1016/j.dld.2024.05.033. Epub 2024 Jun 8.
Inflammatory bowel disease (IBD) includes Crohn's Disease (CD) and Ulcerative Colitis (UC). Correct diagnosis requires the identification of precise morphological features such basal plasmacytosis. However, histopathological interpretation can be challenging, and it is subject to high variability.
The IBD-Artificial Intelligence (AI) project aims at the development of an AI-based evaluation system to support the diagnosis of IBD, semi-automatically quantifying basal plasmacytosis.
A deep learning model was trained to detect and quantify plasma cells on a public dataset of 4981 annotated images. The model was then tested on an external validation cohort of 356 intestinal biopsies of CD, UC and healthy controls. AI diagnostic performance was calculated compared to human gold standard.
The system correctly found that CD and UC samples had a greater prevalence of basal plasma cells with mean number of PCs within ROIs of 38.22 (95 % CI: 31.73, 49.04) for CD, 55.16 (46.57, 65.93) for UC, and 17.25 (CI: 12.17, 27.05) for controls. Overall, OR=4.968 (CI: 1.835, 14.638) was found for IBD compared to normal mucosa (CD: +59 %; UC: +129 %). Additionally, as expected, UC samples were found to have more plasma cells in colon than CD cases.
Our model accurately replicated human assessment of basal plasmacytosis, underscoring the value of AI models as a potential aid IBD diagnosis.
炎症性肠病（IBD）包括克罗恩病（CD）和溃疡性结肠炎（UC）。正确诊断需要识别精确的形态学特征，如基底浆细胞增多。然而，组织病理学解释可能具有挑战性，并且存在很大的变异性。
IBD人工智能（AI）项目旨在开发一种基于AI的评估系统，以支持IBD的诊断，半自动量化基底浆细胞增多。
在一个包含4981张标注图像的公共数据集上训练一个深度学习模型，以检测和量化浆细胞。然后在一个由356例CD、UC和健康对照的肠道活检组成的外部验证队列上对该模型进行测试。将AI的诊断性能与人类金标准进行比较计算。
该系统正确发现，CD和UC样本中基底浆细胞的患病率更高，CD样本感兴趣区域内浆细胞的平均数量为38.22（95%CI：31.73，49.04），UC为55.16（46.57，65.93），对照为17.25（CI：12.17，27.05）。总体而言，与正常黏膜相比，IBD的优势比为4.968（CI：1.835，14.638）（CD：增加59%；UC：增加129%）。此外，正如预期的那样，发现UC样本在结肠中的浆细胞比CD病例更多。
我们的模型准确地复制了人类对基底浆细胞增多的评估，强调了AI模型作为IBD诊断潜在辅助手段的价值。
