Adoption of Artificial Intelligence (AI)-Based Computerized Tomography (CT) Evaluation of Comprehensive Nursing in the Operation Room in Laparoscopy-Guided Radical Surgery of Colon Cancer

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 2180788 - 11
• Main Authors: Duan, Xiuping, Su, Dechun, Yu, Haiwei, Xin, Wei, Wang, Yang, Zhao, Ziming
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2022; Hindawi Limited
• Subjects: Algorithms; Artificial Intelligence; Blood pressure; Cardiovascular disease; Catheters; Colon; Colon cancer; Colonic Neoplasms - diagnostic imaging; Colonic Neoplasms - surgery; Colorectal cancer; Complications; Computed tomography; Constipation; Evaluation; Figure of merit; Gastric cancer; Heart rate; Hospitals; Human error; Humans; Image enhancement; Image processing; Image quality; Image reconstruction; Laparoscopy; Medical imaging; Medical prognosis; Noise; Nursing; Patients; Postoperative; Recovery time; Run time (computers); Statistics; Surgery; Tomography, X-Ray Computed - methods; Tumors; Veins & arteries
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/2180788

This research aimed to discuss the application of traditional nonlocal mean (NLM) algorithm-based computerized tomography (CT) images in intervention evaluation of the nursing for patients performing laparoscopy-guided radical surgery of colon cancer. A total of 100 patients who were diagnosed with colon cancer after enteroscopy and performed laparoscopic radical surgery were chosen as the research objects. They were divided into an observation group (comprehensive nursing in operation room) and a control group (routine nursing), each of which included 50 cases. All cases received CT examination. Meanwhile, the improved traditional NLM (INLM) algorithm was proposed, and the effects of image reconstruction were analyzed to improve the quality of CT images. The result showed that structural similarity index measure (SSIM) and figure of merit (FOM) of INLM were obviously higher than those of filtered back projection (FBP) algorithm and NLM algorithm, and the average running time was significantly less than that of FBP algorithm and NLM algorithm (P < 0.05). The operation time and the amount of intraoperative blood loss of patients in the observation group were both less than those of patients in the control group, and differences had statistical significance (P < 0.05). Besides, the time of getting out of bed, ventilation recovery time, postoperative meal time, stomach tube encumbrance time, and catheter encumbrance time of patients in the observation group were all less than those of patients in the control group, and the differences had statistical significance (P < 0.05). In the observation group, there were 3 cases with postoperative complications, and 2 out of them got incision infection while 1 suffered from constipation. In contrast, there were 9 cases with postoperative complications in the control group, 3 of which were patients with incision infection, and 2 suffered from urinary retention while the other 4 suffered from constipation. According to the above results, the INLM algorithm proposed in this research could improve the image reconstruction accuracy of traditional algorithm, shorten the running time, and enhance the overall diagnostic efficiency. The comprehensive nursing in operation room with laparoscopic radical surgery of colon cancer could improve the cure rate and prognosis of patients, so it was worthy of clinical promotion and application.