Dark-Lumen Magnetic Resonance Image Based on Artificial Intelligence Algorithm in Differential Diagnosis of Colon Cancer

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 8
• Main Authors: Fang, Yujie, Kang, Ting, Yang, Yang, Zi, Yonghong, Lu, Xiong
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial Intelligence; Cancer; Colon; Colon cancer; Colonic Neoplasms - diagnostic imaging; Colonoscopy; Colorectal cancer; Comparative analysis; Diagnosis; Diagnosis, Differential; Dictionaries; Differential diagnosis; Diffusion coefficient; Endoscopy; Error correction; Humans; Magnetic Resonance Imaging; Medical imaging; Medical imaging equipment; Medical research; Medical screening; Medicine, Experimental; Patients; Performance evaluation; Tumors; Wavelet transforms; China
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/4217573

This research was aimed o investigate the application value and diagnostic effect of dark-lumen magnetic resonance imaging (dark-lumen MRI) based on artificial intelligence algorithm on colon cancer. A total of 98 patients with ulcerated colon cancer were selected as the study subjects. All patients underwent colonic endoscopy. The patients were divided into algorithm group (artificial intelligence algorithm processing image group) and control group (conventional method processing image group) according to different dark-lumen MRI processing methods. The detection efficiency of colon cancer was compared between the two groups. It showed that the diagnostic effect of dark-lumen MRI based on artificial intelligence algorithm was significant. The apparent diffusion coefficient (ADC) in the control group was 0.92 ± 0.14 mm2/s (minimum: 0.74, maximum: 1.30), ADC in the algorithm group was 1.55 ± 0.31 mm2/s (minimum: 1.22, maximum: 2.42). The ADC of patients in algorithm group was significantly higher than that of patients in control group, with statistical difference (t = 7.827, P < 0.001). The correct number of cases was 46 and the diagnostic error number was 3 in algorithm group, with accuracy of 93%. The correct number of cases was 41 and the diagnostic error number was 8 in control group, with accuracy of 83%. In comparison, the correct rate was 10% higher in algorithm group, indicating that the diagnostic effect was better in algorithm group. The mean value of invasion depth was 10.42 in the algorithm group and 5.27 in the control group, indicating that the algorithm group was more accurate in the judgment of invasion depth, had a good prospect of clinical application, and had guiding significance for the diagnosis of colon cancer.