  Purpose - To develop and validate a deep learning (DL) framework for the
detection and quantification of drusen and reticular pseudodrusen (RPD) on
optical coherence tomography scans.
  Design - Development and validation of deep learning models for
classification and feature segmentation.
  Methods - A DL framework was developed consisting of a classification model
and an out-of-distribution (OOD) detection model for the identification of
ungradable scans; a classification model to identify scans with drusen or RPD;
and an image segmentation model to independently segment lesions as RPD or
drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with
a self-reported diagnosis of age-related macular degeneration (AMD) and 250
UKBB controls. Drusen and RPD were manually delineated by five retina
specialists. The main outcome measures were sensitivity, specificity, area
under the ROC curve (AUC), kappa, accuracy and intraclass correlation
coefficient (ICC).
  Results - The classification models performed strongly at their respective
tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans
classifier, the OOD model, and the drusen and RPD classification model). The
mean ICC for drusen and RPD area vs. graders was 0.74 and 0.61, respectively,
compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that
the model's sensitivity was close to human performance.
  Conclusions - The models achieved high classification and segmentation
performance, similar to human performance. Application of this robust framework
will further our understanding of RPD as a separate entity from drusen in both
research and clinical settings.
