Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia.
<h4>Purpose</h4>To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.<h4>Methods<...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0274562 |
| _version_ | 1811180750683242496 |
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| author | Leehi Joo Woo Hyun Shim Chong Hyun Suh Su Jin Lim Hwon Heo Woo Seok Kim Eunpyeong Hong Dongsoo Lee Jinkyeong Sung Jae-Sung Lim Jae-Hong Lee Sang Joon Kim |
| author_facet | Leehi Joo Woo Hyun Shim Chong Hyun Suh Su Jin Lim Hwon Heo Woo Seok Kim Eunpyeong Hong Dongsoo Lee Jinkyeong Sung Jae-Sung Lim Jae-Hong Lee Sang Joon Kim |
| author_sort | Leehi Joo |
| collection | DOAJ |
| description | <h4>Purpose</h4>To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.<h4>Methods</h4>This retrospective, observational, single-institution study investigated the diagnostic performance of a deep learning-based automatic WMH volume segmentation to classify the grades of the Fazekas scale and differentiate subcortical vascular dementia. The VUNO Med-DeepBrain was used for the WMH segmentation system. The system for segmentation of WMH was designed with convolutional neural networks, in which the input image was comprised of a pre-processed axial FLAIR image, and the output was a segmented WMH mask and its volume. Patients presented with memory complaint between March 2017 and June 2018 were included and were split into training (March 2017-March 2018, n = 596) and internal validation test set (April 2018-June 2018, n = 204).<h4>Results</h4>Optimal cut-off values to categorize WMH volume as normal vs. mild/moderate/severe, normal/mild vs. moderate/severe, and normal/mild/moderate vs. severe were 3.4 mL, 9.6 mL, and 17.1 mL, respectively, and the AUC were 0.921, 0.956 and 0.960, respectively. When differentiating normal/mild vs. moderate/severe using WMH volume in the test set, sensitivity, specificity, and accuracy were 96.4%, 89.9%, and 91.7%, respectively. For distinguishing subcortical vascular dementia from others using WMH volume, sensitivity, specificity, and accuracy were 83.3%, 84.3%, and 84.3%, respectively.<h4>Conclusion</h4>Deep learning-based automatic WMH segmentation may be an accurate and promising method for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia. |
| first_indexed | 2024-04-11T09:08:30Z |
| format | Article |
| id | doaj.art-96e02c25ee65428fb7a903f432b377fc |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T09:08:30Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-96e02c25ee65428fb7a903f432b377fc2022-12-22T04:32:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01179e027456210.1371/journal.pone.0274562Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia.Leehi JooWoo Hyun ShimChong Hyun SuhSu Jin LimHwon HeoWoo Seok KimEunpyeong HongDongsoo LeeJinkyeong SungJae-Sung LimJae-Hong LeeSang Joon Kim<h4>Purpose</h4>To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.<h4>Methods</h4>This retrospective, observational, single-institution study investigated the diagnostic performance of a deep learning-based automatic WMH volume segmentation to classify the grades of the Fazekas scale and differentiate subcortical vascular dementia. The VUNO Med-DeepBrain was used for the WMH segmentation system. The system for segmentation of WMH was designed with convolutional neural networks, in which the input image was comprised of a pre-processed axial FLAIR image, and the output was a segmented WMH mask and its volume. Patients presented with memory complaint between March 2017 and June 2018 were included and were split into training (March 2017-March 2018, n = 596) and internal validation test set (April 2018-June 2018, n = 204).<h4>Results</h4>Optimal cut-off values to categorize WMH volume as normal vs. mild/moderate/severe, normal/mild vs. moderate/severe, and normal/mild/moderate vs. severe were 3.4 mL, 9.6 mL, and 17.1 mL, respectively, and the AUC were 0.921, 0.956 and 0.960, respectively. When differentiating normal/mild vs. moderate/severe using WMH volume in the test set, sensitivity, specificity, and accuracy were 96.4%, 89.9%, and 91.7%, respectively. For distinguishing subcortical vascular dementia from others using WMH volume, sensitivity, specificity, and accuracy were 83.3%, 84.3%, and 84.3%, respectively.<h4>Conclusion</h4>Deep learning-based automatic WMH segmentation may be an accurate and promising method for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.https://doi.org/10.1371/journal.pone.0274562 |
| spellingShingle | Leehi Joo Woo Hyun Shim Chong Hyun Suh Su Jin Lim Hwon Heo Woo Seok Kim Eunpyeong Hong Dongsoo Lee Jinkyeong Sung Jae-Sung Lim Jae-Hong Lee Sang Joon Kim Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia. PLoS ONE |
| title | Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia. |
| title_full | Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia. |
| title_fullStr | Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia. |
| title_full_unstemmed | Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia. |
| title_short | Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia. |
| title_sort | diagnostic performance of deep learning based automatic white matter hyperintensity segmentation for classification of the fazekas scale and differentiation of subcortical vascular dementia |
| url | https://doi.org/10.1371/journal.pone.0274562 |
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