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In Neuroscience letters

This study was aimed to investigate the possible risk factors of stoma prolapsing infection and neuropsychological problems after colostomy based on the artificial intelligence DiracNet network. 380 patients who underwent colostomy were selected as the research objects. The clinical data of these patients were analyzed, and postoperative follow-ups were performed. The statistics on gender, age, stoma type, stoma location, stoma size, previous medical history, and postoperative chemotherapy of patients were counted. The Chi-square test was utilized to analyze the risk factors associated with stoma prolapsing infection. Computer linear regression analysis was utilized to analyze the risk factors that caused stoma prolapsing infection and the neuropsychological problems of patients. The artificial intelligence DiracNet network was used to extract and analyze the features of patients' intestinal stoma prolapsing infection images. Results: Twenty-six patients had stoma prolapsing infection; the Chi-square test showed that the age, stoma type, stoma size, and stoma prolapsing infection were strongly correlated (P < 0.05), while the gender, stoma location, previous medical history, and postoperative chemotherapy hardly caused prolapsing infection (P > 0.05). The results of the computer linear regression analysis showed that the age, stoma type, and stoma size were three independent risk factors that increased the rate of stoma prolapsing infection (P < 0.05). Patients with stoma prolapsing infection were easy to have neuropsychological problems; the Pittsburgh Sleep Quality Index (PSQI), Hamilton Anxiety Scale (HAMA), and Hamilton Depression Scale (HAMD) scores of patients with stoma prolapsing infection were statistically significant compared with the normal group (P < 0.05). In conclusion, the artificial intelligence DiracNet network could obtain a clear image of the patient's intestinal stoma prolapsing infection and clearly shows the fluid leakage and ulceration of the infected part of the patient's intestinal stoma.

Li Jing, Liu Xiaoyu, Chen Jun


artificial intelligence DiracNet network, computer regression analysis, neuropsychology, stoma prolapsing infection