Fuzzy C-Means Algorithm-Based Adoption of Obturator Nerve Block under Adaptive Ultrasound Imaging for Bladder Tumor

Abstract

This work aimed to study the adoption of obturator nerve block (ONB) based on adaptive medical ultrasound imaging under C-means algorithm in transurethral resection of bladder tumor (TURBT). 120 patients with bladder tumors were diagnosed by C-means algorithm-based ultrasound imaging and were enrolled into group A (epidural anesthesia + resection), group B (general anesthesia), and group C (epidural anesthesia + ONB), each with 40 cases. The accuracy of the detection method, noise level, and complications before and after the operation were compared. All patients received TURBT for treatment. There was no significant difference in the general information of patients in each group ( $P>0.05$ ). As a result, the correct segmentation rate of the tumor region segmented by ultrasound imaging by C-means algorithm reached 95.6%. The incidence of obturator nerve reflex (ONR) in group A (7.5%) was greatly inferior to groups B and C ( $P<0.05$ ). The length of hospital stay in group A was (4.01 ± 1.43) days, which was notably different from groups B and C, with considerable difference among the three ( $P<0.05$ ). In short, the adaptive medical ultrasound imaging under C-means algorithm was more accurate in the diagnosis of bladder tumors. Moreover, ONB can effectively reduce the ONR and the incidence of complications in patients.