https://scholars.lib.ntu.edu.tw/handle/123456789/631155
標題: | Detecting Hydronephrosis Through Ultrasound Images Using State-of-the-Art Deep Learning Models | 作者: | WAN-CHING LIEN Chang, Yi-Chung Chou, Hsin-Hung Lin, Lung-Chun Liu, Yueh-Ping Liu, Li Chan, Yen-Ting Kuan, Feng-Sen |
關鍵字: | Deep Learning; Hydronephrosis; Res-UNet; U-Net; UNet++; Ultrasound; YOLOv4 | 公開日期: | 三月-2023 | 卷: | 49 | 期: | 3 | 來源出版物: | Ultrasound in medicine & biology | 摘要: | The 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/631155 | ISSN: | 03015629 | DOI: | 10.1016/j.ultrasmedbio.2022.10.001 |
顯示於: | 醫學院附設醫院 (臺大醫院) |
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