Advances in decision support for diagnosis and early management of acute leukaemia

Affiliations

¹ Computational Hematology Lab, University Hospital Essen, Essen 45147, Germany; Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen 45147, Germany; Department of Hematology and Oncology, University Hospital Marienhospital, Ruhr University Bochum, Bochum, Germany. Electronic address: amin.turki@uk-essen.de.
² Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen 45147, Germany.
³ Department of Hematology, Blood Neoplasm and Bone Marrow Transplantation, Wrocław Medical University, Wrocław, Poland.

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Amin T Turki et al. Lancet Digit Health. 2024 May.

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. 2024 May;6(5):e300-e301.

doi: 10.1016/S2589-7500(24)00066-9.

¹ Computational Hematology Lab, University Hospital Essen, Essen 45147, Germany; Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen 45147, Germany; Department of Hematology and Oncology, University Hospital Marienhospital, Ruhr University Bochum, Bochum, Germany. Electronic address: amin.turki@uk-essen.de.
² Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen 45147, Germany.
³ Department of Hematology, Blood Neoplasm and Bone Marrow Transplantation, Wrocław Medical University, Wrocław, Poland.

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Evaluation of a machine-learning model based on laboratory parameters for the prediction of acute leukaemia subtypes: a multicentre model development and validation study in France.
Alcazer V, Le Meur G, Roccon M, Barriere S, Le Calvez B, Badaoui B, Spaeth A, Kosmider O, Freynet N, Eveillard M, Croizier C, Chevalier S, Sujobert P. Alcazer V, et al. Lancet Digit Health. 2024 May;6(5):e323-e333. doi: 10.1016/S2589-7500(24)00044-X. Lancet Digit Health. 2024. PMID: 38670741