  This study evaluated generative methods to potentially mitigate AI bias when
diagnosing diabetic retinopathy (DR) resulting from training data imbalance, or
domain generalization which occurs when deep learning systems (DLS) face
concepts at test/inference time they were not initially trained on. The public
domain Kaggle-EyePACS dataset (88,692 fundi and 44,346 individuals, originally
diverse for ethnicity) was modified by adding clinician-annotated labels and
constructing an artificial scenario of data imbalance and domain generalization
by disallowing training (but not testing) exemplars for images of retinas with
DR warranting referral (DR-referable) and from darker-skin individuals, who
presumably have greater concentration of melanin within uveal melanocytes, on
average, contributing to retinal image pigmentation. A traditional/baseline
diagnostic DLS was compared against new DLSs that would use training data
augmented via generative models for debiasing. Accuracy (95% confidence
intervals [CI]) of the baseline diagnostics DLS for fundus images of
lighter-skin individuals was 73.0% (66.9%, 79.2%) vs. darker-skin of 60.5%
(53.5%, 67.3%), demonstrating bias/disparity (delta=12.5%) (Welch t-test
t=2.670, P=.008) in AI performance across protected subpopulations. Using novel
generative methods for addressing missing subpopulation training data
(DR-referable darker-skin) achieved instead accuracy, for lighter-skin, of
72.0% (65.8%, 78.2%), and for darker-skin, of 71.5% (65.2%,77.8%),
demonstrating closer parity (delta=0.5%) in accuracy across subpopulations
(Welch t-test t=0.111, P=.912). Findings illustrate how data imbalance and
domain generalization can lead to disparity of accuracy across subpopulations,
and show that novel generative methods of synthetic fundus images may play a
role for debiasing AI.
