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Artificial Intelligence Technologies in the Diagnosis of Acute Lymphoblastic Leukemia and Minimal Residual Disease

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dc.contributor.authorTupitsyn, N. N.
dc.contributor.authorChernysheva, O. A.
dc.contributor.authorSerebryakova, I. N.
dc.contributor.authorPalladina, A. D.
dc.contributor.authorNikitaev, V. G.
dc.contributor.authorPronichev, A. N.
dc.contributor.authorPolyakov, E. V.
dc.contributor.authorDmitrieva, V. V.
dc.contributor.authorНикитаев, Валентин Григорьевич
dc.contributor.authorПроничев, Александр Николаевич
dc.contributor.authorПоляков, Евгений Валерьевич
dc.contributor.authorДмитриева, Валентина Викторовна
dc.date.accessioned2024-11-29T11:03:15Z
dc.date.available2024-11-29T11:03:15Z
dc.date.issued2021
dc.description.abstract© 2021, Springer Science+Business Media, LLC, part of Springer Nature.The possibilities of using artificial intelligence technologies based on computer microscopy in the diagnosis of acute lymphoblastic leukemia and minimal residual disease have been investigated. The diagnosis “acute lymphoblastic leukemia” is made on the basis of a set of diagnostic studies. Among them, microscopy of bone marrow preparations (morphology) is mandatory. Laser flow cytofluorometry is also among the main techniques used to diagnose acute lymphoblastic leukemia. We propose a procedure for microscopy of bone marrow preparations based on the use of artificial intelligence technologies. The composition of the antibody panel for laser flow cyto-fluorometry is determined from the results of bone marrow preparation microscopy. The system for recognizing bone marrow cells in a microscopic image of a bone marrow preparation is based on the description of the texture features of cells and classification using the support vector method. A reference knowledge base required to analyze the capabilities of the bone marrow cell recognition system has been compiled. The knowledge base includes two sets of images of bone marrow cells. The first is used to establish the possibilities of classifying bone marrow cells in the diagnosis of acute lymphoblastic leukemia. The second is used for differential (clarifying) diagnosis of T- and B-cell acute lymphoblastic leukemias. The conducted experiments have confirmed the high efficiency of the computer microscopy method based on the use of artificial intelligence technologies. The proposed approach can be used as a means of supporting medical decision-making in the diagnosis of acute lymphoblastic leukemia and minimal residual disease.
dc.identifier.citationArtificial Intelligence Technologies in the Diagnosis of Acute Lymphoblastic Leukemia and Minimal Residual Disease / Tupitsyn, N.N. [et al.] // Biomedical Engineering. - 2021. - 10.1007/s10527-021-10038-6
dc.identifier.doi10.1007/s10527-021-10038-6
dc.identifier.urihttps://www.doi.org/10.1007/s10527-021-10038-6
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85099056331&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000748264500013
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/23567
dc.relation.ispartofBiomedical Engineering
dc.titleArtificial Intelligence Technologies in the Diagnosis of Acute Lymphoblastic Leukemia and Minimal Residual Disease
dc.typeArticle
dspace.entity.typePublication
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