Abstract
This work was aimed to study the analgesic effect of pudendal nerve block on obstetrics and gynecology under the guidance of ultrasound image based on optimized fast super resolution reconstructed convolutional neural network (FSRCNN) algorithm. A total of 110 primiparas from hospital who gave birth through vagina were randomly rolled into experimental group (55 cases) and control group (55 cases). The optimized FSRCNN algorithm was constructed, compared with the FSRCNN algorithm and the Bicubic algorithm and applied to 110 cases of maternal patients undergoing perineotomy under ultrasound image-guided pudendal nerve block. Visual analogue scoring (VAS), incision suture pain VAS score, occurrence of complications, puerpera labor time, and newborn weight were recorded and compared, so did Apgar score of newborns, numbness of maternal thigh, recovery of puncture site, and satisfaction of maternal analgesia. The results showed that the peak signal-to-noise ratio (PSNR) of the high-resolution image reconstructed by the FSRCNN algorithm was 32.68 dB and that reconstructed by the optimized FSRCNN algorithm was 32.19 dB. The PSNR of the Bicubic algorithm processed image was 28.51 dB. In the lateral resection of episiotomy in the second stage of labor, the visual analog score (2.3 ± 1.5 points) of the experimental group was inferior to that of the control group (7.1 ± 2.6 points) ( P < 0.05 ). The visual analogue score of stitch pain (1.3 ± 0.8 points) was also inferior to that of the control group (5.2 ± 1.9 points) ( P < 0.05 ). Moreover, the satisfaction of the parturients in the experimental group (9.86 ± 0.41 points) was considerably superior to that of the control group (7.36 ± 1.25 points) ( P < 0.05 ). In short, the optimized FSRCNN algorithm had a short training time and good reconstruction effect. Ultrasound-guided pudendal nerve block had a substantial analgesic effect on the second stage of labor and improved parturients’ satisfaction.
Highlights
Perineotomy is a surgical incision in the perineum and the posterior wall of the vagina, usually performed by a midwife or obstetrician [1]
peak signal-to-noise ratio (PSNR) was selected as a quantitative index of image reconstruction effect. e PSNR of the high-resolution image reconstructed by the fast super resolution reconstructed convolutional neural network (FSRCNN) algorithm was 32.68 dB, the PSNR of the high-resolution image reconstructed by the optimized FSRCNN algorithm was 32.19 dB, and the PSNR of the Bicubic algorithm processed image was 28.51 dB
It showed that increasing the number of convolutional layers can make the neural network become deeper, improve the performance of the model, and make the reconstruction results accurate. e FSRCNN algorithm got 5 × 106 iterations after training for about 200h. e optimized FSRCNN algorithm reached 5 × 106 iterations after training for about 650h. e above data showed that the optimized FSRCNN algorithm was superior to the FSRCNN algorithm and the Bicubic algorithm in terms of training time and reconstruction effect
Summary
Perineotomy is a surgical incision in the perineum and the posterior wall of the vagina, usually performed by a midwife or obstetrician [1]. Perineotomy is usually performed in the second stage of labor to rapidly widen the opening through which the baby passes. During the first and second stages of labor, the fetus’s descent in the birth canal will cause compression on the vagina and perineum, and the expansion of the birth canal will lead to strong stretching and tearing of fascia and subcutaneous tissues [4]. Vaginal nerve blocks block the transmission of perineal pain, allowing pelvic floor muscle tissue to relax completely. It can relieve labor pain and reduce the resistance of fetal delivery, shorten the time of the second stage of labor, and have no effect on uterine autonomic nerve and uterine contractions [6]
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