Image Enhancement Model Based on Deep Learning Applied to the Ureteroscopic Diagnosis of Ureteral Stones during Pregnancy

• Published in: Computational and mathematical methods in medicine 2021-10, Vol.2021, p.9548312-8
• Main Authors: Miao, Xiao-Yan, Miao, Xiao-Nan, Ye, Li-Yin, Cheng, Hong
• Format: Article
• Language: English
• Subjects: Computational Biology; Deep Learning; Diagnosis, Computer-Assisted - statistics & numerical data; Female; Humans; Image Enhancement - methods; Lithotripsy - adverse effects; Lithotripsy - methods; Models, Statistical; Neural Networks, Computer; Pregnancy; Pregnancy Complications - diagnostic imaging; Pregnancy Complications - surgery; Ultrasonography - methods; Ultrasonography - statistics & numerical data; Ureteral Calculi - complications; Ureteral Calculi - diagnostic imaging; Ureteral Calculi - surgery; Ureteroscopy - adverse effects; Ureteroscopy - methods; Ureteroscopy - statistics & numerical data
• ISSN: 1748-670X; 1748-6718
• DOI: 10.1155/2021/9548312

Objective. 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. Methods. 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. Results. 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 (P