Deep Learning-Based Analysis of Efficiency and Surgical Timing for Patients with Cervical Insufficiency Using Transvaginal Ultrasound Images

Abstract

Objective. This work aims to analyze the surgical timing and clinical efficacy of transvaginal cervical ring ligation based on the ultrasound image focus detection of patients with cervical insufficiency (CIC) under the ultrasound image theme generation model. Methods. 134 CIC patients who came to the hospital for ultrasound imaging diagnosis were collected. Observation group was treated with cervical cerclage (CC) and the pregnancy outcome was followed up. Control group was treated conservatively. Results. For patients in the control group, average gestational age was 21.12 ± 2.18 weeks, average cervical length (CL) was 15.54 ± 0.42 mm, and average uterine opening width was 3.06 ± 0.63 mm. In the observation group, average gestational age was 24.45 ± 4.12 weeks, average CL was 17.32 ± 4.09 mm, and average uterine opening width was 0.21 mm. There were significant differences between the two groups ( $P<0.05$ ). There were also significant differences in the degree of uterine orifice dilation between the two groups ( $P<0.05$ ). Pregnancy outcomes of the two groups were compared, and χ2 and $P<0.05$ indicated significant differences. Conclusion. Convolution neural network (CNN) and long short-term memory model (LSTM) algorithm were used to classify patients' ultrasound images, which could effectively improve diagnosis and treatment efficiency. Surgical success rate, clinical outcomes, neonatal survival rate, and clinical effect of observation group were better than those of control group. Cervical ligation was best performed before 17 weeks of pregnancy in CIC.