Department of Ultrasound, Tengzhou Central People's Hospital, No. 181 Xingtan Road, Tengzhou City 277599, Shandong Province, China.
Department of Ultrasound, Zaozhuang Traditional Chinese Medicine Hospital, No. 2666 Taihang Mountain South Road, Xuecheng District, Zaozhuang City 277100 Shandong Province, China.
Comput Math Methods Med. 2022 Mar 23;2022:9170274. doi: 10.1155/2022/9170274. eCollection 2022.
This study was aimed at exploring the application value of three-dimensional (3D) ultrasound based on deep learning and continued nursing health monitoring (CNHM) mode in promoting the recovery of bladder cancer patients after surgery. 60 patients who underwent muscular noninvasive superficial bladder cancer and bladder perfusion treatment were selected as the research objects. The patients were randomly divided into two groups: an experimental group (30 cases) and a control group (30 cases). Patients in the experimental group adopted a CNHM model during the bladder perfusion treatment. The patients in control group adopted ordinary health monitoring mode. All patients underwent 3D ultrasound examination, and all images were processed using the convolutional neural network (CNN) algorithm. All patients were followed up regularly within 12 after the treatment. The imaging data, quality of life, satisfaction, and complications of the two groups of patients were compared in each time period. The ultrasound image processed by the CNN algorithm was clearer than that processed by the original method, showing higher image quality and more prominent lesion features. After 12 months of health monitoring intervention, the overall health status, scores of various functional areas, and score of functional subscales of the experimental group were greatly higher than those of the control group, and the differences were statistically significant ( < 0.05). The incidence of adverse reactions in the experimental group was much lower than that in the control group, and the difference was statistically obvious ( < 0.05). The comparison of the recurrence rate between the two groups of patients in each time period was statistically significant. The satisfaction score of the experimental group was much higher than the score of the control group, showing statistically significant difference ( < 0.05). CNN algorithm showed high application value in 3D ultrasound image processing, and the CNHM model was very beneficial to the postoperative recovery of bladder cancer patients.
本研究旨在探索基于深度学习的三维（3D）超声联合延续护理健康监测（CNHM）模式在促进膀胱癌患者术后康复中的应用价值。选取 60 例行肌层非浸润性表浅膀胱癌及膀胱灌注治疗的患者作为研究对象，将其随机分为实验组（30 例）和对照组（30 例）。实验组患者在膀胱灌注治疗中采用 CNHM 模式，对照组患者采用常规健康监测模式。所有患者均行 3D 超声检查，所有图像均采用卷积神经网络（CNN）算法进行处理。所有患者在治疗后 12 个月内定期进行随访，比较两组患者各时间段的影像学资料、生活质量、满意度及并发症。CNN 算法处理后的超声图像比原始方法处理的更清晰，图像质量更高，病灶特征更突出。经过 12 个月的健康监测干预，实验组患者的整体健康状况、各功能领域评分及功能亚量表评分均明显高于对照组，差异具有统计学意义（ < 0.05）。实验组患者不良反应发生率明显低于对照组，差异具有统计学意义（ < 0.05）。两组患者各时间段的复发率比较，差异均具有统计学意义。实验组患者的满意度评分明显高于对照组，差异具有统计学意义（ < 0.05）。CNN 算法在 3D 超声图像处理中具有较高的应用价值，CNHM 模式对膀胱癌患者术后康复非常有益。
