A Study of Age and Sex Bias in Multiple Instance Learning based Classification of Acute Myeloid Leukemia Subtypes
This addresses bias in medical AI for AML classification, which is crucial for equitable patient care, though it is incremental as it applies existing methods to analyze bias.
The study investigated age and sex bias in Multiple Instance Learning-based classification of Acute Myeloid Leukemia subtypes, finding that females and older patients (72-86 years with RUNX1::RUNX1T1 subtype) were significantly affected by training data imbalances.
Accurate classification of Acute Myeloid Leukemia (AML) subtypes is crucial for clinical decision-making and patient care. In this study, we investigate the potential presence of age and sex bias in AML subtype classification using Multiple Instance Learning (MIL) architectures. To that end, we train multiple MIL models using different levels of sex imbalance in the training set and excluding certain age groups. To assess the sex bias, we evaluate the performance of the models on male and female test sets. For age bias, models are tested against underrepresented age groups in the training data. We find a significant effect of sex and age bias on the performance of the model for AML subtype classification. Specifically, we observe that females are more likely to be affected by sex imbalance dataset and certain age groups, such as patients 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 generating reliable and equitable outcomes in AML genetic subtype classification, ultimately benefiting diverse patient populations.