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AuthorElsayed, Basel
AuthorElshoeibi, Amgad M
AuthorElhadary, Mohamed
AuthorFerih, Khaled
AuthorElsabagh, Ahmed Adel
AuthorRahhal, Alaa
AuthorAbu-Tineh, Mohammad
AuthorAfana, Mohammad S
AuthorAbdulgayoom, Mohammed
AuthorYassin, Mohamed
Available date2023-05-11T05:50:54Z
Publication Date2023-03-16
Publication NameDiagnostics
Identifier10.3390/diagnostics13061123
CitationElsayed, B., Elshoeibi, A. M., Elhadary, M., Ferih, K., Elsabagh, A. A., Rahhal, A., ... & Yassin, M. (2023). Applications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms. Diagnostics, 13(6), 1123.
URIhttp://hdl.handle.net/10576/42576
AbstractPhiladelphia-negative (Ph-) myeloproliferative neoplasms (MPNs) are a group of hematopoietic malignancies identified by clonal proliferation of blood cell lineages and encompasses polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The clinical and laboratory features of Philadelphia-negative MPNs are similar, making them difficult to diagnose, especially in the preliminary stages. Because treatment goals and progression risk differ amongst MPNs, accurate classification and prognostication are critical for optimal management. Artificial intelligence (AI) and machine learning (ML) algorithms provide a plethora of possible tools to clinicians in general, and particularly in the field of malignant hematology, to better improve diagnosis, prognosis, therapy planning, and fundamental knowledge. In this review, we summarize the literature discussing the application of AI and ML algorithms in patients with diagnosed or suspected Philadelphia-negative MPNs. A literature search was conducted on PubMed/MEDLINE, Embase, Scopus, and Web of Science databases and yielded 125 studies, out of which 17 studies were included after screening. The included studies demonstrated the potential for the practical use of ML and AI in the diagnosis, prognosis, and genomic landscaping of patients with Philadelphia-negative MPNs.
Languageen
PublisherMultidisciplinary Digital Publishing Institute (MDPI)
Subjectartificial intelligence
clinical decision support system
convolutional neural networks
deep learning
diagnosis
genomics
machine learning
myeloproliferative neoplasms
prognosis
TitleApplications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms.
TypeArticle
Issue Number6
Volume Number13
ESSN2075-4418
dc.accessType Open Access


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