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

TL;DR

This study examines how age and sex biases in training data affect the performance of Multiple Instance Learning models in classifying AML subtypes, highlighting the importance of balanced datasets for equitable healthcare outcomes.

Contribution

It reveals the presence and impact of age and sex biases in AML classification models and emphasizes the need for inclusive training data to improve model fairness.

Findings

Sex and age biases significantly affect model performance.

Female patients are more impacted by sex imbalance.

Older age groups, especially 72-86 years, are affected by age bias.

Abstract

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.