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Artificial intelligence in renal pathology: Current status and future
Renal biopsy pathology is an essential gold standard for the diagnosis of most kidney diseases. With the increase in the incidence rate of kidney diseases, the lack of renal pathologists, and an imbalance in their distribution, there is an urgent need for a new renal pathological diagnosis model. Ad...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113937/ https://www.ncbi.nlm.nih.gov/pubmed/36378066 http://dx.doi.org/10.17305/bjbms.2022.8318 |
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author | Feng, Chunyue Liu, Fei |
author_facet | Feng, Chunyue Liu, Fei |
author_sort | Feng, Chunyue |
collection | PubMed |
description | Renal biopsy pathology is an essential gold standard for the diagnosis of most kidney diseases. With the increase in the incidence rate of kidney diseases, the lack of renal pathologists, and an imbalance in their distribution, there is an urgent need for a new renal pathological diagnosis model. Advances in artificial intelligence (AI) along with the growing digitization of pathology slides for diagnosis are promising approach to meet the demand for more accurate detection, classification, and prediction of the outcome of renal pathology. AI has contributed substantially to a variety of clinical applications, including renal pathology. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extraction, is increasingly being used in multiple areas of pathology. In this narrative review, we first provide a general description of AI methods, and then discuss the current and prospective applications of AI in the field of renal pathology. Both diagnostic and predictive prognostic applications are covered, emphasizing AI in renal pathology images, predictive models, and 3D in renal pathology. Finally, we outline the challenges associated with the implementation of AI platforms in renal pathology and provide our perspective on how these platforms might change in this field. |
format | Online Article Text |
id | pubmed-10113937 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina |
record_format | MEDLINE/PubMed |
spelling | pubmed-101139372023-04-20 Artificial intelligence in renal pathology: Current status and future Feng, Chunyue Liu, Fei Biomol Biomed Review Renal biopsy pathology is an essential gold standard for the diagnosis of most kidney diseases. With the increase in the incidence rate of kidney diseases, the lack of renal pathologists, and an imbalance in their distribution, there is an urgent need for a new renal pathological diagnosis model. Advances in artificial intelligence (AI) along with the growing digitization of pathology slides for diagnosis are promising approach to meet the demand for more accurate detection, classification, and prediction of the outcome of renal pathology. AI has contributed substantially to a variety of clinical applications, including renal pathology. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extraction, is increasingly being used in multiple areas of pathology. In this narrative review, we first provide a general description of AI methods, and then discuss the current and prospective applications of AI in the field of renal pathology. Both diagnostic and predictive prognostic applications are covered, emphasizing AI in renal pathology images, predictive models, and 3D in renal pathology. Finally, we outline the challenges associated with the implementation of AI platforms in renal pathology and provide our perspective on how these platforms might change in this field. Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina 2023-04-01 2023-03-16 /pmc/articles/PMC10113937/ /pubmed/36378066 http://dx.doi.org/10.17305/bjbms.2022.8318 Text en © 2022 Feng and Liu. https://creativecommons.org/licenses/by/4.0/This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Feng, Chunyue Liu, Fei Artificial intelligence in renal pathology: Current status and future |
title | Artificial intelligence in renal pathology: Current status and future |
title_full | Artificial intelligence in renal pathology: Current status and future |
title_fullStr | Artificial intelligence in renal pathology: Current status and future |
title_full_unstemmed | Artificial intelligence in renal pathology: Current status and future |
title_short | Artificial intelligence in renal pathology: Current status and future |
title_sort | artificial intelligence in renal pathology: current status and future |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113937/ https://www.ncbi.nlm.nih.gov/pubmed/36378066 http://dx.doi.org/10.17305/bjbms.2022.8318 |
work_keys_str_mv | AT fengchunyue artificialintelligenceinrenalpathologycurrentstatusandfuture AT liufei artificialintelligenceinrenalpathologycurrentstatusandfuture |