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Artificial intelligence-aided ultrasound in renal diseases: a systematic review
BACKGROUND: The development of artificial intelligence (AI) techniques has provided a novel strategy for improving the performance of renal ultrasound. To reflect the development of AI methods in renal ultrasound, we aimed to clarify and analyze the state of AI-aided ultrasound research in renal dis...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240007/ https://www.ncbi.nlm.nih.gov/pubmed/37284081 http://dx.doi.org/10.21037/qims-22-1428 |
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author | Liang, Xiaowen Du, Meng Chen, Zhiyi |
author_facet | Liang, Xiaowen Du, Meng Chen, Zhiyi |
author_sort | Liang, Xiaowen |
collection | PubMed |
description | BACKGROUND: The development of artificial intelligence (AI) techniques has provided a novel strategy for improving the performance of renal ultrasound. To reflect the development of AI methods in renal ultrasound, we aimed to clarify and analyze the state of AI-aided ultrasound research in renal diseases. METHODS: PRISMA 2020 guidelines have been used to guide all processes and results. AI-aided renal ultrasound studies (for both image segmentation and disease diagnosis) published up to June 2022 were screened through the databases of PubMed and Web of Science. Accuracy/Dice similarity coefficient (DICE), the area under the curve (AUC), sensitivity/specificity, and other indications were applied as evaluation parameters. The PROBAST was used to assess the risk of bias in the studies screened. RESULTS: Of 364 articles, 38 studies were analyzed, and could be divided into AI-aided diagnosis or prediction related studies (28/38) and image segmentation related studies (10/38). The output of these 28 studies involved differential diagnosis of local lesions, disease grading of, automatic diagnosis, and diseases prediction. The median values of accuracy and AUC were 0.88 and 0.96, respectively. Overall, 86% of the AI-aided diagnosis or prediction models were classified as high risk. An unclear source of data, inadequate sample size, inappropriate analysis methods, and lack of rigorous external validation were found to be the most frequent and critical risk factors in AI-aided renal ultrasound studies. CONCLUSIONS: AI is a potential technique in the ultrasound diagnosis of different types of renal diseases, but the reliability and availability need to be strengthened. The use of AI-aided ultrasound in chronic kidney disease and quantitative hydronephrosis diagnosis will be a promising possibility. The size and quality of sample data, rigorous external validation, and adherence to guidelines and standards should be considered in further studies. |
format | Online Article Text |
id | pubmed-10240007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-102400072023-06-06 Artificial intelligence-aided ultrasound in renal diseases: a systematic review Liang, Xiaowen Du, Meng Chen, Zhiyi Quant Imaging Med Surg Review Article BACKGROUND: The development of artificial intelligence (AI) techniques has provided a novel strategy for improving the performance of renal ultrasound. To reflect the development of AI methods in renal ultrasound, we aimed to clarify and analyze the state of AI-aided ultrasound research in renal diseases. METHODS: PRISMA 2020 guidelines have been used to guide all processes and results. AI-aided renal ultrasound studies (for both image segmentation and disease diagnosis) published up to June 2022 were screened through the databases of PubMed and Web of Science. Accuracy/Dice similarity coefficient (DICE), the area under the curve (AUC), sensitivity/specificity, and other indications were applied as evaluation parameters. The PROBAST was used to assess the risk of bias in the studies screened. RESULTS: Of 364 articles, 38 studies were analyzed, and could be divided into AI-aided diagnosis or prediction related studies (28/38) and image segmentation related studies (10/38). The output of these 28 studies involved differential diagnosis of local lesions, disease grading of, automatic diagnosis, and diseases prediction. The median values of accuracy and AUC were 0.88 and 0.96, respectively. Overall, 86% of the AI-aided diagnosis or prediction models were classified as high risk. An unclear source of data, inadequate sample size, inappropriate analysis methods, and lack of rigorous external validation were found to be the most frequent and critical risk factors in AI-aided renal ultrasound studies. CONCLUSIONS: AI is a potential technique in the ultrasound diagnosis of different types of renal diseases, but the reliability and availability need to be strengthened. The use of AI-aided ultrasound in chronic kidney disease and quantitative hydronephrosis diagnosis will be a promising possibility. The size and quality of sample data, rigorous external validation, and adherence to guidelines and standards should be considered in further studies. AME Publishing Company 2023-04-20 2023-06-01 /pmc/articles/PMC10240007/ /pubmed/37284081 http://dx.doi.org/10.21037/qims-22-1428 Text en 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Review Article Liang, Xiaowen Du, Meng Chen, Zhiyi Artificial intelligence-aided ultrasound in renal diseases: a systematic review |
title | Artificial intelligence-aided ultrasound in renal diseases: a systematic review |
title_full | Artificial intelligence-aided ultrasound in renal diseases: a systematic review |
title_fullStr | Artificial intelligence-aided ultrasound in renal diseases: a systematic review |
title_full_unstemmed | Artificial intelligence-aided ultrasound in renal diseases: a systematic review |
title_short | Artificial intelligence-aided ultrasound in renal diseases: a systematic review |
title_sort | artificial intelligence-aided ultrasound in renal diseases: a systematic review |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240007/ https://www.ncbi.nlm.nih.gov/pubmed/37284081 http://dx.doi.org/10.21037/qims-22-1428 |
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