Cerebral Angiography under Artificial Intelligence Algorithm in the Design of Nursing Cooperation Plan for Intracranial Aneurysm Patients in Craniotomy Clipping.

This research was to investigate the value of indocyanine green angiography (ICGA) based on maximum interclass variance (Otsu) method in the nursing plan of intracranial aneurysm clipping (ICAC) for intracranial aneurysm patients. An Otsu algorithm was selected to optimize the original images with the optimal threshold. In addition, the algorithm was applied to ICGA images of 86 patients with intracranial aneurysms, who were randomly divided into an experimental group (using ICGA + ICAC+ perioperative nursing) and a control group (ICAC + conventional nursing), to observe the clinical indicators, treatment, complications, nursing satisfaction, and quality of life of patients in two groups. The results showed that the mean square error (MSE), structural similarity (SSIM), and shape error (SE) were 3.71, 0.84, and 0.47, respectively. The length of hospital stay in the experimental group (19.9 ± 3.5 days) was significantly shorter than that in the control group (23.2 ± 3.0 days), the rate of excellent treatment was significantly higher than that in the control group, and the incidence of complications was lower. WHOQOL-BREF scores of the two groups after nursing intervention were higher than before, and the score in the experimental group was higher than the control group. In addition, the nursing satisfaction was also significantly higher in the experimental group, and the difference was statistically significant ( < 0.05). In conclusion, ICGA based on the Otsu method could effectively evaluate the cerebrovascular morphology during craniotomy and ICAP and improve the surgical efficacy. Combined with perioperative nursing intervention, it could greatly reduce the incidence of postoperative complications, improve the treatment effect and quality of life, and enhance the long-term prognosis.