ID | 116817 |
Author |
Bando, Hiroshi
Kanaiso Hospital|Tokushima University|Integrative Medicine Japan
KAKEN Search Researchers
Ogura, K
Kanaiso Hospital
Obonai, T
Kanaiso Hospital
Kawata, T
Kanaiso Hospital
Kato, Y
Kanaiso Hospital
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Keywords | Three-dimension (3-D) image analysis
Synapse vincent
Artificial intelligence (AI)
Deep learning
Atrophy of renal cortex
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Content Type |
Journal Article
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Description | Background: Recently, the development of information and communication technology (ICT) has been remarkable utilizing artificial intelligence (AI) technology with deep learning. Three-dimension (3-D) image analysis technology has developed using computerized tomography (CT), magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA). Among them, SYNAPSE VINCENT system (Fujifilm, Japan) is known for its predominance.
Patient and Method: The patient is a 52-year-old female with type 2 diabetes mellitus (T2DM), who was suspected to have space occupying lesion (SOL) in the right kidney. Method included the investigation of enhanced abdominal CT with analysis of SYNAPSE VINCENT. Results: The detail analysis showed some findings as follows: i) coronal view of bilateral kidney shows well-enhanced left adrenal tumour, no apparent of right renal tumour, and atrophy of renal cortex, ii) the image rotated 30 degrees showed same findings, iii) the image rotated 180 degrees also showed atrophy of reverse side of right kidney. Discussion: In this case, the background of the atrophy of renal cortex has not been apparent, but it might be from diabetic nephropathy (DN). The application of VINCENT has expanded to various organs, such as liver, pancreas, biliary tract, and others, expecting augmentation of articulate data using 3D image analysis. |
Journal Title |
SunText Review of Case Reports & Images
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ISSN | 27664589
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Publisher | SunText Reviews
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Volume | 3
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Issue | 1
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Start Page | 139
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Published Date | 2022-01-10
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Rights | This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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DOI (Published Version) | |
URL ( Publisher's Version ) | |
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language |
eng
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TextVersion |
Publisher
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departments |
Medical Sciences
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