Diagnosis and prognosis of epidemic inflammatory bowel disease under convolutional neural network algorithm and nonlinear equation model

Abstract

this study was to analyze the effect of optimization algorithm based on convolutional neural network (CNN) on the image quality of magnetic resonance enterography (MRE) for small intestine, and explore the diagnosis and prognosis of epidemic inflammation bowel disease (IBD) with the MRE under the optimization algorithm and nonlinear equation model. The residual network structure (RNS) was incorporated into the CNN algorithm to establish an optimization algorithm, which was compared with other algorithms in terms of the peak signal to noise ratio (PSNR), structural similarity (SSIM), and high frequency error norm (HFEN). 392 patients suspected as IBD in the Gastroenterology Department were selected as the research objects. They were divided into a Crohn’s disease (CD) group (243 cases), an ulcerative colitis (UC) group (78 cases), an indeterminate enteritis (IDE) group (49 cases), and a non-IBD group (22 cases). The intestinal manifestations, clinical manifestations, MRE imaging manifestations, and gastrointestinal lesion distribution of patients in different groups were compared to analyze the diagnostic value of MRE for epidemic IBD. It was found that the optimized CNN algorithm showed better PSNR, SSIM, and HFEN than other algorithms. The percentages of patients with diarrhea in the CD group and UC group were 67.90% and 100%, respectively. The pediatric ulcerative colitis activity index (PUCAI) score of patients in the UC group showed that the number of patients with mild activity was at most 40 (51.28%). The Crohn’s disease activity index (CDAI) score showed that there were 197 of patients in the CD group (81.07%) with severe activity. In the CD group, the numbers of patients suffered with lesions in proximal colon, terminal ileum, and upper gastrointestinal tract were 125 (51.44%), 127 (52.26%), and 132 (54.32%), respectively. In the UC group, there were 32 (41.03%), 51 (65.38%), and 25 (32.05%) patients with lesions in the rectum, distal colon, and proximal colon, respectively. The diagnostic sensitivity and specificity of MRE for epidemic IBD was 95.18% and 46.7%, respectively. It indicated that the image quality of the optimization algorithm based on the CNN algorithm was improved greatly. Under the nonlinear equation model, the image characteristics of MRE in patients with different diseases were various, and the diseased parts were greatly different. MRE showed high sensitivity and specificity in the diagnosis of epidemic IBD. This study could provide a reference basis for the clinical diagnosis and prognosis of patients with epidemic IBD.