Applicability of oculomics for individual risk prediction: Repeatability
  and robustness of retinal Fractal Dimension using DART and AutoMorph
Justin Engelmann Diana Moukaddem Lucas Gago Niall Strang Miguel O. Bernabeu
Purpose: To investigate whether Fractal Dimension (FD)-based oculomics could
be used for individual risk prediction by evaluating repeatability and
robustness. Methods: We used two datasets: Caledonia, healthy adults imaged
multiple times in quick succession for research (26 subjects, 39 eyes, 377
colour fundus images), and GRAPE, glaucoma patients with baseline and follow-up
visits (106 subjects, 196 eyes, 392 images). Mean follow-up time was 18.3
months in GRAPE, thus it provides a pessimistic lower-bound as vasculature
could change. FD was computed with DART and AutoMorph. Image quality was
assessed with QuickQual, but no images were initially excluded. Pearson,
Spearman, and Intraclass Correlation (ICC) were used for population-level
repeatability. For individual-level repeatability, we introduce measurement
noise parameter {\lambda} which is within-eye Standard Deviation (SD) of FD
measurements in units of between-eyes SD. Results: In Caledonia, ICC was 0.8153
for DART and 0.5779 for AutoMorph, Pearson/Spearman correlation (first and last
image) 0.7857/0.7824 for DART, and 0.3933/0.6253 for AutoMorph. In GRAPE,
Pearson/Spearman correlation (first and next visit) was 0.7479/0.7474 for DART,
and 0.7109/0.7208 for AutoMorph (all p<0.0001). Median {\lambda} in Caledonia
without exclusions was 3.55\% for DART and 12.65\% for AutoMorph, and improved
to up to 1.67\% and 6.64\% with quality-based exclusions, respectively. Quality
exclusions primarily mitigated large outliers. Worst quality in an eye
correlated strongly with {\lambda} (Pearson 0.5350-0.7550, depending on dataset
and method, all p<0.0001). Conclusions: Repeatability was sufficient for
individual-level predictions in heterogeneous populations. DART performed
better on all metrics and might be able to detect small, longitudinal changes,
highlighting the potential of robust methods.