Department of Radiation Oncology, The First People's Hospital of Fuyang (Fuyang First Affiliated Hospital of Zhejiang Chinese Medical University Ben Giang College), Hangzhou, China 311400.
Department of Endocrinology, The First People's Hospital of Fuyang (Fuyang First Affiliated Hospital of Zhejiang Chinese Medical University Ben Giang College), Hangzhou, China 311400.
Comput Math Methods Med. 2021 Oct 29;2021:9548312. doi: 10.1155/2021/9548312. eCollection 2021.
To explore the image enhancement model based on deep learning on the effect of ureteroscopy with double J tube placement and drainage on ureteral stones during pregnancy. We compare the clinical effect of ureteroscopy with double J tube placement on pregnancy complicated with ureteral stones and use medical imaging to diagnose the patient's condition and design a treatment plan.
The image enhancement model is constructed using deep learning and implemented for quality improvement in terms of image clarity. In the way, the relationship of the media transmittance and the image with blurring artifacts was established, and the model can estimate the ureteral stone predicted map of each region. Firstly, we proposed the evolution-based detail enhancement method. Then, the feature extraction network is used to capture blurring artifact-related features. Finally, the regression subnetwork is used to predict the media transmittance in the local area. Eighty pregnant patients with ureteral calculi treated in our hospital were selected as the research object and were divided into a test group and a control group according to the random number table method, 40 cases in each group. The test group underwent ureteroscopy double J tube placement, and the control group underwent ureteroscopy lithotripsy. Combined with the ultrasound scan results of the patients before and after the operation, the operation time, time to get out of bed, and hospitalization time of the two groups of patients were compared. The operation success rate and the incidence of complications within 1 month after surgery were counted in the two groups of patients.
We are able to improve the quality of the images prior to medical diagnosis. The total effective rate of the observation group was 100.0%, which is higher than that of the control group (90.0%). The difference between the two groups was statistically significant ( < 0.05). The adverse reaction rate in the observation group was 5.0%, which was lower than 17.5% in the control group. The difference between the two groups was statistically significant ( < 0.05). The comparison results are then prepared.
The image enhancement model based on deep learning is able to improve medical diagnosis which can assist radiologists to better locate the ureteral stones. Based on our method, double J tube placement under ureteroscopy has a significant effect on the treatment of ureteral stones during pregnancy, and it has good safety and is worthy of widespread application.
探讨基于深度学习的图像增强模型对妊娠合并输尿管结石行输尿管镜下双 J 管置入及引流术的效果。我们比较了妊娠合并输尿管结石行输尿管镜下双 J 管置入术的临床效果，并利用医学影像学对患者病情进行诊断并设计治疗方案。
利用深度学习构建图像增强模型，实现图像清晰度的质量提升。在这种方式中，建立了介质透过率与带有模糊伪影的图像之间的关系，该模型可以预测每个区域的输尿管结石预测图。首先，我们提出了基于进化的细节增强方法。然后，使用特征提取网络来捕获与模糊伪影相关的特征。最后，使用回归子网络预测局部的介质透过率。选取我院收治的 80 例妊娠合并输尿管结石患者作为研究对象，采用随机数字表法分为观察组和对照组，各 40 例。观察组行输尿管镜下双 J 管置入术，对照组行输尿管镜碎石术。结合患者手术前后的超声扫描结果，比较两组患者的手术时间、下床时间和住院时间。统计两组患者的手术成功率及术后 1 个月内并发症发生率。
我们能够提高医学诊断前的图像质量。观察组总有效率为 100.0%，高于对照组的 90.0%，两组比较差异有统计学意义（ < 0.05）。观察组不良反应发生率为 5.0%，低于对照组的 17.5%，两组比较差异有统计学意义（ < 0.05）。
基于深度学习的图像增强模型能够提高医学诊断的质量，有助于放射科医生更好地定位输尿管结石。基于我们的方法，输尿管镜下双 J 管置入术治疗妊娠合并输尿管结石效果显著，且安全性良好，值得广泛应用。
