Artificial Intelligence for computer-aided leukemia diagnostics
Thieme. All rights reserved..
The manual examination of blood and bone marrow specimens for leukemia patients is time-consuming and limited by intra- and inter-observer variance. The development of AI algorithms for leukemia diagnostics requires high-quality sample digitization and reliable annotation of large datasets. Deep learning-based algorithms using these datasets attain human-level performance for some well-defined, clinically relevant questions such as the blast character of cells. Methods such as multiple - instance - learning allow predicting diagnoses from a collection of leukocytes, but are more data-intensive. Using "explainable AI" methods can make the prediction process more transparent and allow users to verify the algorithm's predictions. Stability and robustness analyses are necessary for routine application of these algorithms, and regulatory institutions are developing standards for this purpose. Integrated diagnostics, which link different diagnostic modalities, offer the promise of even greater accuracy but require more extensive and diverse datasets.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:148 |
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| Enthalten in: |
Deutsche medizinische Wochenschrift (1946) - 148(2023), 17 vom: 07. Sept., Seite 1108-1112 |
| Sprache: |
Deutsch |
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| Weiterer Titel: |
Künstliche Intelligenz für die computerunterstützte Leukämiediagnostik |
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| Beteiligte Personen: |
Matek, Christian [VerfasserIn] |
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| Themen: |
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| Anmerkungen: |
Date Completed 25.08.2023 Date Revised 06.10.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1055/a-1965-7044 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 100 | 1 | |a Matek, Christian |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Artificial Intelligence for computer-aided leukemia diagnostics |
| 246 | 3 | 3 | |a Künstliche Intelligenz für die computerunterstützte Leukämiediagnostik |
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| 500 | |a Date Completed 25.08.2023 | ||
| 500 | |a Date Revised 06.10.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Thieme. All rights reserved. | ||
| 520 | |a The manual examination of blood and bone marrow specimens for leukemia patients is time-consuming and limited by intra- and inter-observer variance. The development of AI algorithms for leukemia diagnostics requires high-quality sample digitization and reliable annotation of large datasets. Deep learning-based algorithms using these datasets attain human-level performance for some well-defined, clinically relevant questions such as the blast character of cells. Methods such as multiple - instance - learning allow predicting diagnoses from a collection of leukocytes, but are more data-intensive. Using "explainable AI" methods can make the prediction process more transparent and allow users to verify the algorithm's predictions. Stability and robustness analyses are necessary for routine application of these algorithms, and regulatory institutions are developing standards for this purpose. Integrated diagnostics, which link different diagnostic modalities, offer the promise of even greater accuracy but require more extensive and diverse datasets | ||
| 650 | 4 | |a English Abstract | |
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Marr, Carsten |e verfasserin |4 aut | |
| 700 | 1 | |a von Bergwelt-Baildon, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Spiekermann, Karsten |e verfasserin |4 aut | |
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