1. Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver
- Author
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Tae Young Shin, Hyunsuk Kim, Joong-Hyup Lee, Jong-Suk Choi, Hyun-Seok Min, Hyungjoo Cho, Kyungwook Kim, Geon Kang, Jungkyu Kim, Sieun Yoon, Hyungyu Park, Yeong Uk Hwang, Hyo Jin Kim, Miyeun Han, Eunjin Bae, Jong Woo Yoon, Koon Ho Rha, and Yong Seong Lee
- Subjects
artificial intelligence ,polycystic kidney diseases ,Diseases of the genitourinary system. Urology ,RC870-923 - Abstract
Purpose: Volumetry is used in polycystic kidney and liver diseases (PKLDs), including autosomal dominant polycystic kidney disease (ADPKD), to assess disease progression and drug efficiency. However, since no rapid and accurate method for volumetry has been developed, volumetry has not yet been established in clinical practice, hindering the development of therapies for PKLD. This study presents an artificial intelligence (AI)-based volumetry method for PKLD. Materials and Methods: The performance of AI was first evaluated in comparison with ground-truth (GT). We trained a V-net-based convolutional neural network on 175 ADPKD computed tomography (CT) segmentations, which served as the GT and were agreed upon by 3 experts using images from 214 patients analyzed with volumetry. The dice similarity coefficient (DSC), interobserver correlation coefficient (ICC), and Bland–Altman plots of 39 GT and AI segmentations in the validation set were compared. Next, the performance of AI on the segmentation of 50 random CT images was compared with that of 11 PKLD specialists based on the resulting DSC and ICC. Results: The DSC and ICC of the AI were 0.961 and 0.999729, respectively. The error rate was within 3% for approximately 95% of the CT scans (error
- Published
- 2020
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