This study focused on the segmentation effects of an artificial intelligence-based algorithm of CT images, to detect the bile duct injury (BDI) after laparoscopic cholecystectomy (LC) under dezocine anesthesia. This study was based on the maximum between-class variance (Otsu) algorithm; it introduced the image grayscale mapping method to increase the accuracy of the target area segmentation within the CT image and compare the segmentation effect with the threshold segmentation and the regional growth segmentation algorithm. 46 patients treated with laparoscopic cholecystectomy (LC) were used as research objects, and all patients were inspected in the abdominal CT examination. According to the anesthetic drug selection, patients were divided into control group (conventional anesthesia) and dezocine group (conventional anesthesia + dezocine), with 23 cases in each group. And it compared the difference between the respiratory recovery time, the wake time, the tube time, and the postoperative 3, 6, 12, and 24 h after surgery, and complication after LC evaluation of bile duct injury (BDI). It was found that the algorithm in this study can segment the target area in CT image accurately. Compared with the threshold segmentation and region growing segmentation algorithms, its Dice similarity coefficient (DSC) and Jaccard similarity coefficient (JSC) were higher (). There was no statistically significant difference in postoperative spontaneous breathing recovery time, wake-up time, and extubation time between the dezocine group and the control group (), but in the dezocine group, the visual analogue scale (VAS) scores at 3, 6, 12, and 24 hours after the surgery were lower (). 27 patients developed BDI after the surgery, and they were classified as per the Strasberg classification standard. It was found that 6 cases were evaluated as type A, 4 cases were type B, 2 cases were type C, 6 cases were type D, and 9 cases were type E. It was concluded that the algorithm in this study can segment the target area of the CT image accurately, assisting the doctor in diagnosis. The use of dezocine before LC can effectively relieve patients’ postoperative pain. This study provides a basis for the diagnosis and treatment of gallbladder disease and the detection of complications.

中文翻译：

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基于人工智能的下腔静脉图像在地佐辛麻醉下检测腹腔镜胆囊切除术后胆管损伤

本研究侧重于基于人工智能的 CT 图像算法的分割效果，以检测地佐辛麻醉下腹腔镜胆囊切除术 (LC) 后的胆管损伤 (BDI)。本研究基于最大类间方差（Otsu）算法；引入图像灰度映射方法，提高CT图像内目标区域分割的精度，并将分割效果与阈值分割和区域生长分割算法进行比较。46例腹腔镜胆囊切除术（LC）患者为研究对象，所有患者均行腹部CT检查。根据麻醉药物选择，将患者分为对照组（常规麻醉）和地佐辛组（常规麻醉+地佐辛），每组23例。比较呼吸恢复时间、苏醒时间、插管时间与术后3、6、12、24 h的差异，以及LC评估胆管损伤（BDI）后并发症的差异。发现本研究中的算法可以准确地分割出CT图像中的目标区域。与阈值分割和区域生长分割算法相比，其Dice相似系数（DSC）和Jaccard相似系数（JSC）更高（发现本研究中的算法可以准确地分割出CT图像中的目标区域。与阈值分割和区域生长分割算法相比，其Dice相似系数（DSC）和Jaccard相似系数（JSC）更高（发现本研究中的算法可以准确地分割出CT图像中的目标区域。与阈值分割和区域生长分割算法相比，其Dice相似系数（DSC）和Jaccard相似系数（JSC）更高（）。地佐辛组与对照组术后自主呼吸恢复时间、苏醒时间、拔管时间差异无统计学意义（)，但在地佐辛组中，术后 3、6、12 和 24 小时的视觉模拟量表 (VAS) 评分较低（）。27 例患者术后出现 BDI，按照 Strasberg 分类标准进行分类。发现评估为A型6例，B型4例，C型2例，D型6例，E型9例。 结论本研究算法可以分割目标准确定位CT图像区域，辅助医生诊断。LC前使用地佐辛可有效缓解患者术后疼痛。本研究为胆囊疾病的诊治和并发症的检测提供了依据。