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Matek, C. ; Marr, C. ; von Bergwelt-Baildon, M.* ; Spiekermann, K.*

Künstliche Intelligenz für die computerunterstützte Leukämiediagnostik.

Artificial Intelligence for computer-aided leukemia diagnostics.

Dtsch. Med. Wochenschr. 148, 1108-1112 (2023)
Verlagsversion DOI PMC
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Ai In Medical Image Analysis ; Computer-aided Diagnostics ; Cytomorphology ; Leukemia Diagnostics
Sprache deutsch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0012-0472
e-ISSN 1439-4413
Zeitschrift Deutsche Medizinische Wochenschrift - DMW
Quellenangaben Band: 148, Heft: 17, Seiten: 1108-1112 Artikelnummer: , Supplement: ,
Verlag Thieme
Verlagsort Rudigerstr 14, D-70469 Stuttgart, Germany
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540007-001
DOI 10.1055/a-1965-7044
WOS ID 001054012300012
Scopus ID 85168571686
PubMed ID 37611575
Erfassungsdatum 2023-10-06
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