WAN-CHING LIENChang, Yi-ChungYi-ChungChangChou, Hsin-HungHsin-HungChouLin, Lung-ChunLung-ChunLinLiu, Yueh-PingYueh-PingLiuLiu, LiLiLiuChan, Yen-TingYen-TingChanKuan, Feng-SenFeng-SenKuan2023-05-182023-05-182023-0303015629https://scholars.lib.ntu.edu.tw/handle/123456789/631155The goal of this study was to assess the feasibility of three models for detecting hydronephrosis through ultrasound images using state-of-the-art deep learning algorithms. The diagnosis of hydronephrosis is challenging because of varying and non-specific presentations. With the characteristics of ready accessibility, no radiation exposure and repeated assessments, point-of-care ultrasound becomes a complementary diagnostic tool for hydronephrosis; however, inter-observer variability still exists after time-consuming training. Artificial intelligence has the potential to overcome the human limitations. A total of 3462 ultrasound frames for 97 patients with hydronephrosis confirmed by the expert nephrologists were included. One thousand six hundred twenty-eight ultrasound frames were also extracted from the 265 controls who had normal renal ultrasonography. We built three deep learning models based on U-Net, Res-UNet and UNet++ and compared their performance. We applied pre-processing techniques including wiping the background to lessen interference by YOLOv4 and standardizing image sizes. Also, post-processing techniques such as adding filter for filtering the small effusion areas were used. The Res-UNet algorithm had the best performance with an accuracy of 94.6% for moderate/severe hydronephrosis with substantial recall rate, specificity, precision, F1 measure and intersection over union. The Res-UNet algorithm has the best performance in detection of moderate/severe hydronephrosis. It would decrease variability among sonographers and improve efficiency under clinical conditions.enDeep Learning; Hydronephrosis; Res-UNet; U-Net; UNet++; Ultrasound; YOLOv4[SDGs]SDG3journal article10.1016/j.ultrasmedbio.2022.10.001365096162-s2.0-85147015761