Shortcut learning leads to sex bias in deep learning models for photoacoustic tomography.

ABSTRACT:

Purpose

Shortcut learning has been identified as a source of algorithmic unfairness in medical imaging artificial intelligence (AI), but its impact on photoacoustic tomography (PAT), particularly concerning sex bias, remains underexplored. This study investigates this issue using peripheral artery disease (PAD) diagnosis as a specific clinical application.

Methods

To examine the potential for sex bias due to shortcut learning in convolutional neural network (CNNs) and assess how such biases might affect diagnostic predictions, we created training and test datasets with varying PAD prevalence between sexes. Using these datasets, we explored (1) whether CNNs can classify the sex from imaging data, (2) how sex-specific prevalence shifts impact PAD diagnosis performance and unde

SUBMITTER: Knopp M 

PROVIDER: S-EPMC12226672 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature