Elsafty_Reports_of_Myeloid_Neoplasms_2024

dataset

posted on 2024-10-25, 20:29 authored by Ahmed ElsaftyAhmed Elsafty

The aim of the collected dataset (Elsafty_Reports_of_Myeloid_Neoplasms_2024) is to provide specialized, world-class, and comprehensive templates for myeloid neoplasms that serve purposes in Hematopathology practice, teaching/training/examination, and automation, including verification and validation of relevant automated systems. This validated, up-to-date, 100% accurate, machine-readable, and novel/unprecedented dataset has been meticulously reviewed word-by-word by both Hematopathologists and attending physicians from various specialties who detect or diagnose such cases at multiple international medical centres. Such meticulous review, referencing the WHO 5th edition, the international consensus classification of myeloid neoplasms and acute leukemias, along with other relevant esteemed publications, justifies the complexity and diversity of the dataset while maintaining benchmark scientific quality.

The online web-based software developed for the automated generation of CBC and PBS review narrative interpretation, as well as for complete personalized Hematopathology CPC (https://cbctst.com), demonstrates the utility of this dataset in supporting the validation of such clinical-fit tools, which are requested to assist senior Hematopathologists by saving time and effort, addressing workforce shortages and stress, and minimizing liability and clerical errors.

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AI-powered pathology automation automated hematology diagnostics AI-driven hematopathology digital pathology solutions advanced hematologic neoplasm diagnostics peripheral blood smear automation automated chromosome analysis interpretation cytogenetics automated interpretation JAK2 V617F mutation interpretation BCR-ABL1 kinase domain mutation interpretation molecular monitoring of chronic myeloid leukemia (CML)digital pathology education machine learning in cancer diagnostics enhancing diagnostic accuracy cost-effectiveness of AI in pathology addressing pathologist shortages automation solutions for pathology labs AI-assisted diagnostics digital pathology assistants AI-powered pathology tools automated reporting in hematopathology improving diagnostic precision deep learning for hematopathology diagnostics automated CBC interpretation hematology testing methods diagnostic hematology services leukemia and lymphoma diagnostics next-generation sequencing (NGS) in hematopathology molecular diagnostics for hematologic malignancies monitoring CML with molecular tools CML treatment response assessment myelodysplastic syndrome (MDS) evaluation myeloproliferative neoplasms (MPN) assessments essential thrombocythemia (ET) diagnostic protocols polycythemia vera (PV) evaluation myelofibrosis diagnostic criteria leukoerythroblastosis identification diagnosing leukocytosis and leukopenia iron deficiency anemia assessment blast cell identification promyelocyte recognition RBC morphology WBC differentiation techniques dysplasia in myelodysplastic syndromes atypical lymphocytes in infections PathOlOgics innovations BCR-ABL1 monitoring in CML,left shift in bacterial infections mutation analysis in myeloproliferative neoplasms NGS for mutation profiling diagnostic workup for leukemia evaluating anemia types thrombocytosis and thrombocytopenia assessment acute leukemia diagnostics ASXL1 mutation assessment BCOR and BCORL1 gene assessment CBFB-MYH11 fusion assessment CSF3R mutation assessment DDX41 mutation assessment DEK-NUP214 fusion assessment DNMT3A mutation assessment ETV6 gene assessment EZH2 mutation assessment FLT3 mutation assessment GATA2 gene assessment GNAS gene mutation assessment IDH1 and IDH2 mutation assessment IKZF1 gene assessments KAT6A-CREBBP fusion assessment KIT gene mutation assessment KRAS mutation assessment MLLT3-KMT2A fusion assessment NF1 mutation assessment NPM1 mutation assessment NRAS mutation assessment PHF6 gene mutation assessment PML-RARA fusion assessment SETBP1 mutation assessment RUNX1 mutation assessment RAD21 mutation assessment PTPN11 mutation assessments SF3B1 mutation assessments SMC1A and SMC3 mutation assessment SRSF2 mutation assessment STAG2 mutation assessment ZRSR2 gene mutation assessments WT1 mutation assessment U2AF1 mutation assessment TP53 mutation assessment TET2 mutation assessment automated cytogenetics

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