Imaging Features of Artificial Intelligence Algorithm in the Analysis of Cerebral Protective Effect of Craniotomy Hematoma Removal under Propofol Anesthesia in Patients with Chronic Subdural Hematoma

Objective. The aim of this work was to study the cerebral protective effect of craniotomy hematoma removal under propofol anesthesia based on the artificial intelligence algorithm analysis of the changes in imaging characteristics of chronic subdural hematoma (CSDH) patients. Methods. A total of 60 CSDH patients who were treated in hospital were recruited and were randomly rolled into an experimental group and a control group, with 30 people in each group. Patients in the experimental group were treated with propofol anesthesia + craniotomy hematoma removal, while those in the control group were treated with conventional anesthesia + craniotomy hematoma removal. Head CT examinations were performed on the next day, one week, one month, three months, and six months after the operation. A two-dimensional empirical mode decomposition (BEMD) algorithm was used for edge detection and denoising of brain CT images of CSDH patients. Then, the amount of hematoma was calculated, and the Markwalder grading was performed to evaluate the neurological function. The number of recurrence and reoperation cases within six months of follow-up was collected. Results. 1. The quality of CT images was remarkably improved after processing with artificial intelligence algorithms. 2. The amount of hematoma in the experimental group was remarkably lower than that in the control group at January, March, and June after surgery (12.89 ± 2.12 VS 20.32 ± 16.41; 7.55 ± 4.13 VS 15.88 ± 14.22; 3.39 ± 3.79 VS 6.55 ± 3.69, P