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Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver
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 esta...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Urological Association
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7606119/ https://www.ncbi.nlm.nih.gov/pubmed/33135401 http://dx.doi.org/10.4111/icu.20200086 |
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author | Shin, Tae Young Kim, Hyunsuk Lee, Joong-Hyup Choi, Jong-Suk Min, Hyun-Seok Cho, Hyungjoo Kim, Kyungwook Kang, Geon Kim, Jungkyu Yoon, Sieun Park, Hyungyu Hwang, Yeong Uk Kim, Hyo Jin Han, Miyeun Bae, Eunjin Yoon, Jong Woo Rha, Koon Ho Lee, Yong Seong |
author_facet | Shin, Tae Young Kim, Hyunsuk Lee, Joong-Hyup Choi, Jong-Suk Min, Hyun-Seok Cho, Hyungjoo Kim, Kyungwook Kang, Geon Kim, Jungkyu Yoon, Sieun Park, Hyungyu Hwang, Yeong Uk Kim, Hyo Jin Han, Miyeun Bae, Eunjin Yoon, Jong Woo Rha, Koon Ho Lee, Yong Seong |
author_sort | Shin, Tae Young |
collection | PubMed |
description | 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<1%, 46.2%; 1%≤error<3%, 48.7%). Compared with the specialists, AI showed moderate performance. Furthermore, an outlier in our results confirmed that even PKLD specialists can make mistakes in volumetry. CONCLUSIONS: PKLD volumetry using AI was fast and accurate. AI performed comparably to human specialists, suggesting its use may be practical in clinical settings. |
format | Online Article Text |
id | pubmed-7606119 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Korean Urological Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-76061192020-11-05 Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver Shin, Tae Young Kim, Hyunsuk Lee, Joong-Hyup Choi, Jong-Suk Min, Hyun-Seok Cho, Hyungjoo Kim, Kyungwook Kang, Geon Kim, Jungkyu Yoon, Sieun Park, Hyungyu Hwang, Yeong Uk Kim, Hyo Jin Han, Miyeun Bae, Eunjin Yoon, Jong Woo Rha, Koon Ho Lee, Yong Seong Investig Clin Urol Original Article 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<1%, 46.2%; 1%≤error<3%, 48.7%). Compared with the specialists, AI showed moderate performance. Furthermore, an outlier in our results confirmed that even PKLD specialists can make mistakes in volumetry. CONCLUSIONS: PKLD volumetry using AI was fast and accurate. AI performed comparably to human specialists, suggesting its use may be practical in clinical settings. The Korean Urological Association 2020-11 2020-10-27 /pmc/articles/PMC7606119/ /pubmed/33135401 http://dx.doi.org/10.4111/icu.20200086 Text en © The Korean Urological Association, 2020 http://creativecommons.org/licenses/by-nc/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Shin, Tae Young Kim, Hyunsuk Lee, Joong-Hyup Choi, Jong-Suk Min, Hyun-Seok Cho, Hyungjoo Kim, Kyungwook Kang, Geon Kim, Jungkyu Yoon, Sieun Park, Hyungyu Hwang, Yeong Uk Kim, Hyo Jin Han, Miyeun Bae, Eunjin Yoon, Jong Woo Rha, Koon Ho Lee, Yong Seong Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver |
title | Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver |
title_full | Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver |
title_fullStr | Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver |
title_full_unstemmed | Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver |
title_short | Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver |
title_sort | expert-level segmentation using deep learning for volumetry of polycystic kidney and liver |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7606119/ https://www.ncbi.nlm.nih.gov/pubmed/33135401 http://dx.doi.org/10.4111/icu.20200086 |
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