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Accurate classification of Acute Myeloid Leukemia (AML) subtypes is crucialfor clinical decision-making and patient care. In this study, we investigatethe potential presence of age and sex bias in AML subtype classification usingMultiple Instance Learning (MIL) architectures. To that end, we train multipleMIL models using different levels of sex imbalance in the training set andexcluding certain age groups. To assess the sex bias, we evaluate theperformance of the models on male and female test sets. For age bias, modelsare tested against underrepresented age groups in the training data. We find asignificant effect of sex and age bias on the performance of the model for AMLsubtype classification. Specifically, we observe that females are more likelyto be affected by sex imbalance dataset and certain age groups, such aspatients with 72 to 86 years of age with the RUNX1::RUNX1T1 genetic subtype,are significantly affected by an age bias present in the training data.Ensuring inclusivity in the training data is thus essential for generatingreliable and equitable outcomes in AML genetic subtype classification,ultimately benefiting diverse patient populations.

A Study of Age and Sex Bias in Multiple Instance Learning based Classification of Acute Myeloid Leukemia Subtypes