Artificial intelligence in kidney transplant pathology
© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature..
BACKGROUND: Artificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology.
AIM: Summarize the current state of research and limitations in the field of AI in kidney transplant pathology diagnostics and provide a future outlook.
MATERIALS AND METHODS: Literature search in PubMed and Web of Science using the search terms "deep learning", "transplant", and "kidney". Based on these results and studies cited in the identified literature, a selection was made of studies that have a histopathological focus and use AI to improve kidney transplant diagnostics.
RESULTS AND CONCLUSION: Many studies have already made important contributions, particularly to the automation of the quantification of some histopathological lesions in nephropathology. This likely can be extended to automatically quantify all relevant lesions for a kidney transplant, such as Banff lesions. Important limitations and challenges exist in the collection of representative data sets and the updates of Banff classification, making large-scale studies challenging. The already positive study results make future AI support in kidney transplant pathology appear likely.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Pathologie (Heidelberg, Germany) - (2024) vom: 10. Apr. |
| Sprache: |
Deutsch |
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| Weiterer Titel: |
Künstliche Intelligenz in der Nierentransplantationspathologie |
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| Beteiligte Personen: |
Bülow, Roman David [VerfasserIn] |
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| Links: |
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| Themen: |
Computer assistance |
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| Anmerkungen: |
Date Revised 10.04.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1007/s00292-024-01324-7 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370874285 |
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| 520 | |a © 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature. | ||
| 520 | |a BACKGROUND: Artificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology | ||
| 520 | |a AIM: Summarize the current state of research and limitations in the field of AI in kidney transplant pathology diagnostics and provide a future outlook | ||
| 520 | |a MATERIALS AND METHODS: Literature search in PubMed and Web of Science using the search terms "deep learning", "transplant", and "kidney". Based on these results and studies cited in the identified literature, a selection was made of studies that have a histopathological focus and use AI to improve kidney transplant diagnostics | ||
| 520 | |a RESULTS AND CONCLUSION: Many studies have already made important contributions, particularly to the automation of the quantification of some histopathological lesions in nephropathology. This likely can be extended to automatically quantify all relevant lesions for a kidney transplant, such as Banff lesions. Important limitations and challenges exist in the collection of representative data sets and the updates of Banff classification, making large-scale studies challenging. The already positive study results make future AI support in kidney transplant pathology appear likely | ||
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| 650 | 4 | |a Datenintegration | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Histomorphometry | |
| 650 | 4 | |a Kidney transplantation | |
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| 700 | 1 | |a Amann, Kerstin |e verfasserin |4 aut | |
| 700 | 1 | |a Boor, Peter |e verfasserin |4 aut | |
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