Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans

Abstract

Aims: Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and diabetic retinopathy (DR) patients, to assist in disease prevention via early detection before the disease progresses to a "wet AMD" pathology or diabetic macular edema (DME), requiring treatment. We propose a proof-of-concept AI-based app to help predict fluid via a "fluid score", prevent fluid progression, and provide personalized, serial monitoring, in the context of predictive, preventive, and personalized medicine (PPPM) for patients at risk of retinal fluid complications.

Methods: The app comprises a convolutional neural network-Vision Transformer (CNN-ViT)-based segmentation deep learning (DL) network, trained on a small dataset of 100 training images (augmented to 992 images) from the Singapore Epidemiology of Eye Diseases (SEED) study, together with a CNN-based classification network trained on 8497 images, that can detect fluid vs. non-fluid optical coherence tomography (OCT) scans. Both networks are validated on external datasets.

Results: Internal testing for our segmentation network produced an IoU score of 83.0% (95% CI = 76.7-89.3%) and a DICE score of 90.4% (86.3-94.4%); for external testing, we obtained an IoU score of 66.7% (63.5-70.0%) and a DICE score of 78.7% (76.0-81.4%). Internal testing of our classification network produced an area under the receiver operating characteristics curve (AUC) of 99.18%, and a Youden index threshold of 0.3806; for external testing, we obtained an AUC of 94.55%, and an accuracy of 94.98% and an F1 score of 85.73% with Youden index.

Conclusion: We have developed an AI-based app with an alternative transformer-based segmentation algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring, and could allow for the generation of retrospective data to research into the varied use of treatments for AMD and DR. The modular system of our app can be scaled to add more iterative features based on user feedback for more efficient monitoring. Further study and scaling up of the algorithm dataset could potentially boost its usability in a real-world clinical setting.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00301-5.

Keywords: Age-related macular degeneration; Anti-vascular endothelial growth factor; Computer-aided detection; Deep learning; Diabetic macular edema; Diabetic retinopathy; Ophthalmology; Optical coherence tomography; Predictive, preventive, and personalized OCT; Retinal fluid.

Fig. 1

Flowchart of the overall segmentation and classification algorithm development process. Larger images of the model architectures shown in this image are included in supplementary data (Supplementary Figures 1 and 2)

Fig. 2

Segmentation results of internal and external validation cohorts (SEED and Korean (Severance Hospital)) with 95% confidence intervals (CI) shown in brackets

Fig. 3

Segmentation result visualization of samples from the internal and external validation cohorts. Green is true positive regions, red is false negative regions, and blue is false positive regions. Area of fluid (in mm²) for the B-scan is shown on the left-hand side of the image

Fig. 4

Metrics for classification performance on internal and external testing datasets of the Kaggle dataset and Westmead Hospital dataset (numbers displayed in the table are in percentages). Receiver Operating Characteristics (ROC) curves of the internal validation testing set and the external validation testing set are as shown

Fig. 5

Segmentation visualization (retinal layers in green, fluid areas in red) and classification result (“fluid score”) of the CADe fluid detector on the internal and external validation sets with representative B-scan outputs from both the segmentation and classification algorithms (CADe algorithm)

Fig. 6

Example output of the segmentation and classification algorithm for a DME patient OCT volume of 25 B-scans from the external validation dataset. Segmented retinal layers are in green and segmented fluids are in red

Fig. 7

Example output of the segmentation and classification algorithm for a healthy patient OCT Volume of 25 B-scans from the external validation dataset. Segmented retinal layers are in green and segmented fluids are in red

Fig. 8

Flowchart of the CADe algorithm and app in the context of PPPM. Examples of the web pages are provided in Supplementary Figures 3 and 4