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Künstliche Intelligenz für die computerunterstützte Leukämiediagnostik.
Artificial Intelligence for computer-aided leukemia diagnostics.
Dtsch. Med. Wochenschr. 148, 1108-1112 (2023)
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.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Ai In Medical Image Analysis ; Computer-aided Diagnostics ; Cytomorphology ; Leukemia Diagnostics
Language
german
Publication Year
2023
HGF-reported in Year
2023
ISSN (print) / ISBN
0012-0472
e-ISSN
1439-4413
Quellenangaben
Volume: 148,
Issue: 17,
Pages: 1108-1112
Publisher
Thieme
Publishing Place
Rudigerstr 14, D-70469 Stuttgart, Germany
Reviewing status
Peer reviewed
Institute(s)
Institute of AI for Health (AIH)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-540007-001
WOS ID
001054012300012
Scopus ID
85168571686
PubMed ID
37611575
Erfassungsdatum
2023-10-06