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Prediction of Recurrent Urinary Tract Infection in Paediatric Patients by Deep Learning Analysis of (99m)Tc-DMSA Renal Scan

Purpose: Tc-99m dimercaptosuccinic acid ((99m)Tc-DMSA) renal scan is an important tool for the assessment of childhood urinary tract infection (UTI), vesicoureteral reflux (VUR), and renal scarring. We evaluated whether a deep learning (DL) analysis of (99m)Tc-DMSA renal scans could predict the recu...

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Autores principales: Lee, Hyunjong, Yoo, Beongwoo, Baek, Minki, Choi, Joon Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870906/
https://www.ncbi.nlm.nih.gov/pubmed/35204516
http://dx.doi.org/10.3390/diagnostics12020424
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author Lee, Hyunjong
Yoo, Beongwoo
Baek, Minki
Choi, Joon Young
author_facet Lee, Hyunjong
Yoo, Beongwoo
Baek, Minki
Choi, Joon Young
author_sort Lee, Hyunjong
collection PubMed
description Purpose: Tc-99m dimercaptosuccinic acid ((99m)Tc-DMSA) renal scan is an important tool for the assessment of childhood urinary tract infection (UTI), vesicoureteral reflux (VUR), and renal scarring. We evaluated whether a deep learning (DL) analysis of (99m)Tc-DMSA renal scans could predict the recurrence of UTI better than conventional clinical factors. Methods: the subjects were 180 paediatric patients diagnosed with UTI, who underwent immediate post-therapeutic (99m)Tc-DMSA renal scans. The primary outcome was the recurrence of UTI during the follow-up period. For the DL analysis, a convolutional neural network (CNN) model was used. Age, sex, the presence of VUR, the presence of cortical defects on the (99m)Tc-DMSA renal scan, split renal function (SRF), and DL prediction results were used as independent factors for predicting recurrent UTI. The diagnostic accuracy for predicting recurrent UTI was statistically compared between independent factors. Results: The sensitivity, specificity and accuracy for predicting recurrent UTI were 44.4%, 88.9%, and 82.2% by the presence of VUR; 44.4%, 76.5%, and 71.7% by the presence of cortical defect; 74.1%, 80.4%, and 79.4% by SRF (optimal cut-off = 45.93%); and 70.4%, 94.8%, and 91.1% by the DL prediction results. There were no significant differences in sensitivity between all independent factors (p > 0.05, for all). The specificity and accuracy of the DL prediction results were significantly higher than those of the other factors. Conclusion: DL analysis of (99m)Tc-DMSA renal scans may be useful for predicting recurrent UTI in paediatric patients. It is an efficient supportive tool to predict poor prognosis without visually demonstrable cortical defects in (99m)Tc-DMSA renal scans.
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spelling pubmed-88709062022-02-25 Prediction of Recurrent Urinary Tract Infection in Paediatric Patients by Deep Learning Analysis of (99m)Tc-DMSA Renal Scan Lee, Hyunjong Yoo, Beongwoo Baek, Minki Choi, Joon Young Diagnostics (Basel) Article Purpose: Tc-99m dimercaptosuccinic acid ((99m)Tc-DMSA) renal scan is an important tool for the assessment of childhood urinary tract infection (UTI), vesicoureteral reflux (VUR), and renal scarring. We evaluated whether a deep learning (DL) analysis of (99m)Tc-DMSA renal scans could predict the recurrence of UTI better than conventional clinical factors. Methods: the subjects were 180 paediatric patients diagnosed with UTI, who underwent immediate post-therapeutic (99m)Tc-DMSA renal scans. The primary outcome was the recurrence of UTI during the follow-up period. For the DL analysis, a convolutional neural network (CNN) model was used. Age, sex, the presence of VUR, the presence of cortical defects on the (99m)Tc-DMSA renal scan, split renal function (SRF), and DL prediction results were used as independent factors for predicting recurrent UTI. The diagnostic accuracy for predicting recurrent UTI was statistically compared between independent factors. Results: The sensitivity, specificity and accuracy for predicting recurrent UTI were 44.4%, 88.9%, and 82.2% by the presence of VUR; 44.4%, 76.5%, and 71.7% by the presence of cortical defect; 74.1%, 80.4%, and 79.4% by SRF (optimal cut-off = 45.93%); and 70.4%, 94.8%, and 91.1% by the DL prediction results. There were no significant differences in sensitivity between all independent factors (p > 0.05, for all). The specificity and accuracy of the DL prediction results were significantly higher than those of the other factors. Conclusion: DL analysis of (99m)Tc-DMSA renal scans may be useful for predicting recurrent UTI in paediatric patients. It is an efficient supportive tool to predict poor prognosis without visually demonstrable cortical defects in (99m)Tc-DMSA renal scans. MDPI 2022-02-06 /pmc/articles/PMC8870906/ /pubmed/35204516 http://dx.doi.org/10.3390/diagnostics12020424 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Hyunjong
Yoo, Beongwoo
Baek, Minki
Choi, Joon Young
Prediction of Recurrent Urinary Tract Infection in Paediatric Patients by Deep Learning Analysis of (99m)Tc-DMSA Renal Scan
title Prediction of Recurrent Urinary Tract Infection in Paediatric Patients by Deep Learning Analysis of (99m)Tc-DMSA Renal Scan
title_full Prediction of Recurrent Urinary Tract Infection in Paediatric Patients by Deep Learning Analysis of (99m)Tc-DMSA Renal Scan
title_fullStr Prediction of Recurrent Urinary Tract Infection in Paediatric Patients by Deep Learning Analysis of (99m)Tc-DMSA Renal Scan
title_full_unstemmed Prediction of Recurrent Urinary Tract Infection in Paediatric Patients by Deep Learning Analysis of (99m)Tc-DMSA Renal Scan
title_short Prediction of Recurrent Urinary Tract Infection in Paediatric Patients by Deep Learning Analysis of (99m)Tc-DMSA Renal Scan
title_sort prediction of recurrent urinary tract infection in paediatric patients by deep learning analysis of (99m)tc-dmsa renal scan
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870906/
https://www.ncbi.nlm.nih.gov/pubmed/35204516
http://dx.doi.org/10.3390/diagnostics12020424
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