Department of Orthopedics, Quzhou People's Hospital, Quzhou, Zhejiang 324000, China.
J Healthc Eng. 2022 Mar 22;2022:2779686. doi: 10.1155/2022/2779686. eCollection 2022.
The preoperative qualitative and hierarchical diagnosis of intervertebral foramen stenosis is very important for clinicians to explore the effect of multimodal analgesia nursing on pain control after spinal fusion and to formulate treatment strategies and patients' health recovery. However, there are still many problems in this aspect, and there is a lack of relevant research and effective methods to assist clinicians in diagnosis. Therefore, to improve the accuracy of computer-aided diagnosis of intervertebral foramen stenosis and the work efficiency of doctors, a deep learning automatic grading algorithm of intervertebral foramen stenosis image is proposed in this study. The image of intervertebral foramen was extracted from the MRI image of sagittal spine, and the image was preprocessed. 86 patients with spinal fusion treated in our hospital, specifically from May 2018 to May 2020, were randomly divided into the control group (routine analgesic nursing) and the multimodal group (multimodal analgesic nursing), with 43 cases in each group. The pain control effect and satisfaction of the two groups were observed. The results after multimodal analgesia nursing show that the VASs of the multimodal group at different time points were significantly lower than those of the control group ( < 0.05); the satisfaction score of pain control in the multimodal group was significantly higher than that in the control group ( < 0.05). Multimodal analgesia nursing for patients undergoing spinal fusion can effectively reduce the degree of postoperative pain and improve the effect of pain control and satisfaction with pain control, which is worthy of promotion.
术前对椎间孔狭窄进行定性和分级诊断，对于临床医生探索脊柱融合术后多模式镇痛护理对疼痛控制的效果，制定治疗策略和患者健康恢复非常重要。然而，这方面仍然存在许多问题，缺乏相关研究和有效的方法来协助临床医生进行诊断。因此，为了提高计算机辅助诊断椎间孔狭窄的准确性和医生的工作效率，本研究提出了一种基于深度学习的椎间孔狭窄图像自动分级算法。从矢状位脊柱 MRI 图像中提取椎间孔图像，并对图像进行预处理。随机将我院 2018 年 5 月至 2020 年 5 月收治的 86 例脊柱融合患者分为对照组（常规镇痛护理）和多模式组（多模式镇痛护理），每组 43 例。观察两组患者的疼痛控制效果和满意度。多模式镇痛护理后的结果表明，多模式组在不同时间点的 VAS 明显低于对照组（ < 0.05）；多模式组疼痛控制满意度明显高于对照组（ < 0.05）。多模式镇痛护理可有效降低脊柱融合术后患者的疼痛程度，提高疼痛控制效果和对疼痛控制的满意度，值得推广。
