Wavelet Transform-Based Ultrasound Image Enhancement Algorithm for Guided Gynecological Laparoscopy Imaging of Local Anesthetics in Perioperative Gynecological Laparoscopy

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Abstract

This paper aimed to study the application of local anesthetics combined with transversus abdominis plane (TAP) block in gynecological laparoscopy (GLS) surgery during perioperative period under the guidance of ultrasound image enhanced by the wavelet transform image enhancement (WTIE) algorithm. 56 patients who underwent GLS surgery in hospital were selected and classified as the infiltrating group and block group. The puncture needle was guided by ultrasound images under WTIE algorithm, and 0.375% ropivacaine was adopted to block TAP. The results showed that the dosage of propofol in the infiltrating group (313.23 ± 19.67 mg) was remarkably inferior to the infiltrating group (377.67 ± 21.56 mg) P<0.05. The hospitalization time of patients in the infiltrating group (2.14 ± 0.18 days) was obviously shorter than that of the infiltrating group (3.23 ± 0.27 days) P<0.05. 3 h, 6 h, and 12 h after the operation, the visual analogue scores (3.82 ± 1.58 points, 2.97 ± 1.53 points, and 1.38 ± 0.57 points) of the patients in the infiltration group were considerably higher than the infiltrating group (2.31 ± 1.46 points, 1.06 ± 1.28 points, and 0.95 ± 0.43 points) P<0.05. 3 h, 6 h, and 12 h after the operation, the number of patients in the infiltrating group who used tramadol for salvage analgesia (2 cases, 1 case, and 1 case) was notably less than that in the infiltration group (9 cases, 7 cases, and 3 cases) P<0.05. In short, local anesthetics combined with TAP block can reduce postoperative VAS score and postoperative nausea and vomiting (PONV) score, which also reduced the incidence of postoperative analgesia.

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APA

Sheng, B., Yan, Q., Zhao, X., & Yang, W. (2021). Wavelet Transform-Based Ultrasound Image Enhancement Algorithm for Guided Gynecological Laparoscopy Imaging of Local Anesthetics in Perioperative Gynecological Laparoscopy. Journal of Healthcare Engineering, 2021. https://doi.org/10.1155/2021/5169803

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