Therapeutic Effect of Cervical Cerclage on Cervical Insufficiency during Pregnancy Analyzed by Magnetic Resonance Image under Neural Network Algorithm

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Abstract

Objective. This research was developed to investigate the effect of magnetic resonance imaging (MRI) analysis based on neural network algorithm for cervical ligation in the treatment of cervical insufficiency. Methods. 44 patients who were suspected to be pregnant with cervical insufficiency and needed cervical ligation were selected. MR imaging analysis was performed before cervical ligation. MR images were analyzed based on the back propagation neural network (BPNN) algorithm, and patients were randomly divided into experimental group and control group. Preoperative MRI analysis was performed in the experimental group, while simple transvaginal ultrasonography was used to diagnose cervical insufficiency in the control group. Then, postoperative fetal preservation time, vaginal bleeding rate, and infection rate within one week after surgery were compared between the two groups. Results. Based on experience and experimental testing, the relevant parameters were set as follows. The number of particles n = 50, the inertia weight ω = 0.9, and c1 = c2 = 2. The weight range of the output layer of the neural network was [−1, 1], the target error e = 10−5, and the maximum number of iteration steps was 3,000. Compared with the control group, the experimental group’s postoperative bleeding rate and infection probability were substantially reduced, while the normal delivery rate was substantially increased ( P < 0.05 ). Conclusion. MR image analysis based on neural network algorithm played an important role in cervical cerclage surgery. The image map showed the local anatomy clearly, increasing the success rate of the operation and improving the prognosis of the patient.

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