Neural Network-Based Histologic Remission Prediction In Ulcerative
  Colitis
Yemin li Zhongcheng Liu Xiaoying Lou Mirigual Kurban Miao Li Jie Yang Kaiwei Che Jiankun Wang Max Q. -H Meng Yan Huang Qin Guo Pinjin Hu
BACKGROUND & AIMS: Histological remission (HR) is advocated and considered as
a new therapeutic target in ulcerative colitis (UC). Diagnosis of histologic
remission currently relies on biopsy; during this process, patients are at risk
for bleeding, infection, and post-biopsy fibrosis. In addition, histologic
response scoring is complex and time-consuming, and there is heterogeneity
among pathologists. Endocytoscopy (EC) is a novel ultra-high magnification
endoscopic technique that can provide excellent in vivo assessment of glands.
Based on the EC technique, we propose a neural network model that can assess
histological disease activity in UC using EC images to address the above
issues. The experiment results demonstrate that the proposed method can assist
patients in precise treatment and prognostic assessment.
  METHODS: We construct a neural network model for UC evaluation. A total of
5105 images of 154 intestinal segments from 87 patients undergoing EC treatment
at a center in China between March 2022 and March 2023 are scored according to
the Geboes score. Subsequently, 103 intestinal segments are used as the
training set, 16 intestinal segments are used as the validation set for neural
network training, and the remaining 35 intestinal segments are used as the test
set to measure the model performance together with the validation set.
  RESULTS: By treating HR as a negative category and histologic activity as a
positive category, the proposed neural network model can achieve an accuracy of
0.9, a specificity of 0.95, a sensitivity of 0.75, and an area under the curve
(AUC) of 0.81.
  CONCLUSION: We develop a specific neural network model that can distinguish
histologic remission/activity in EC images of UC, which helps to accelerate
clinical histological diagnosis.
  keywords: ulcerative colitis; Endocytoscopy; Geboes score; neural network.