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The subjectivity of morphological assessment and the overlapping pathological features of different subtypes of myeloproliferative neoplasms (MPNs) make accurate diagnosis challenging. To improve the pathological assessment of MPNs, we developed a diagnosis model (fusion model) based on the combination of bone marrow whole-slide images (deep learning [DL] model) and clinical parameters (clinical model). Thousand and fifty-one MPN and non-MPN patients were divided into the training, internal testing and one internal and two external validation cohorts (the combined validation cohort).

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Key Laboratory of Molecular Diagnosis and Treatment of Hematologic Diseases of Shanxi Province, Taiyuan, China.

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Myeloproliferative neoplasms (MPNs) are a group of chronic hematologic malignancies that lead to morbidity and early mortality due to thrombotic complications and progression to acute leukemia. Clinical and mutational risk factors have been demonstrated to predict outcomes in patients with MPNs and are used commonly to guide therapeutic decisions, including allogenic stem cell transplant, in myelofibrosis. Adolescents and young adults (AYA, age ≤45 years) comprise less than 10% of all MPN patients and have unique clinical and therapeutic considerations.

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