Table 1

Predictive capabilities of each model in the convolutional neural network

Model inputsUWF colourUWF FAFGC-IPLOCTAOCT/OCTA
quantitative data
Patient dataAUC on test set
(95% CI)
UWF colourX0.450 (0.282 to 0.592)
UWF FAFX0.618 (0.462 to 0.773)
OCTAX0.582 (0.440 to 0.724)
GC-IPLX0.809 (0.700 to 0.919)
Quantitative data and patient dataXX0.754 (0.625 to 0.882)
All imagesXXXX0.829 (0.719 to 0.939)
All images and quantitative dataXXXXX0.830 (0.726 to 0.940)
OCTA and GC-IPLXX0.828 (0.718 to 0.938)
All images and all dataXXXXXX0.836 (0.729 to 0.943)
GC-IPL, OCTA, quantitative data and patient dataXXXX0.832 (0.724 to 0.940)
GC-IPL, quantitative data and patient data*XXX0.841 (0.739 to 0.943)
  • AUC on validation and test set figures describe the performance of each model on predicting the probability of symptomatic AD diagnosis for each individual eye in the independent validation set. AUC values represent average performance over epochs 50–100. An ‘x’ indicates that the input(s) were included in the model described in that row.

  • *Indicates best-performing model.

  • AD, Alzheimer’s disease; AUC, area under the receiver operating characteristic curve; FAF, fundus autofluorescence; GC-IPL, ganglion cell-inner plexiform layer thickness maps; OCTA, optical coherence tomography angiography; UWF, ultra-widefield color images.