Automated Detection of Posterior Vitreous Detachment on OCT Using Computer Vision and Deep Learning Algorithms

Abstract

Objective: To develop automated algorithms for the detection of posterior vitreous detachment (PVD) using OCT imaging.

Design: Evaluation of a diagnostic test or technology.

Subjects: Overall, 42 385 consecutive OCT images (865 volumetric OCT scans) obtained with Heidelberg Spectralis from 865 eyes from 464 patients at an academic retina clinic between October 2020 and December 2021 were retrospectively reviewed.

Methods: We developed a customized computer vision algorithm based on image filtering and edge detection to detect the posterior vitreous cortex for the determination of PVD status. A second deep learning (DL) image classification model based on convolutional neural networks and ResNet-50 architecture was also trained to identify PVD status from OCT images. The training dataset consisted of 674 OCT volume scans (33 026 OCT images), while the validation testing set consisted of 73 OCT volume scans (3577 OCT images). Overall, 118 OCT volume scans (5782 OCT images) were used as a separate external testing dataset.

Main outcome measures: Accuracy, sensitivity, specificity, F1-scores, and area under the receiver operator characteristic curves (AUROCs) were measured to assess the performance of the automated algorithms.

Results: Both the customized computer vision algorithm and DL model results were largely in agreement with the PVD status labeled by trained graders. The DL approach achieved an accuracy of 90.7% and an F1-score of 0.932 with a sensitivity of 100% and a specificity of 74.5% for PVD detection from an OCT volume scan. The AUROC was 89% at the image level and 96% at the volume level for the DL model. The customized computer vision algorithm attained an accuracy of 89.5% and an F1-score of 0.912 with a sensitivity of 91.9% and a specificity of 86.1% on the same task.

Conclusions: Both the computer vision algorithm and the DL model applied on OCT imaging enabled reliable detection of PVD status, demonstrating the potential for OCT-based automated PVD status classification to assist with vitreoretinal surgical planning.

Financial disclosures: Proprietary or commercial disclosure may be found after the references.

Keywords: AI, artificial intelligence; AUROC, area under the receiver operator characteristic curve; Automated detection; CNN, convolutional neural network; DL, deep learning; Deep Learning; ILM, internal limiting membrane; OCT; PVD, posterior vitreous detachment; Posterior vitreous detachment; ViT, vision transformers.

Figure 1

Flowchart summary for the 2 posterior vitreous detachment (PVD) automated detection methods implemented on OCT imaging: customized computer vision algorithm (upper); deep learning convolutional neural network (CNN) model (lower).

Figure 2

Stepwise image processing illustration for customized posterior vitreous cortex localization algorithm. A, Original OCT B-scan image of a patient without a complete posterior vitreous detachment. B, Gaussian blur filter from OpenCV is applied to the entire image (blue mask). C, D, The internal limiting membrane (ILM) is located using the customized algorithm (orange line). E, Retinal area below the ILM is masked out (orange mask) and only relevant areas are kept for further posterior vitreous cortex localization. F, Gaussian blur filter from OpenCV is applied to the vitreous area (blue mask). G, H, The posterior vitreous cortex (if present in the image) is detected and located. Customized distance metrics are calculated on the detected segments and compared against heuristic thresholds to determine if the posterior vitreous cortex is present in the image.

Figure 3

Receiving operator characteristic curves and precision-recall (PR) curves at the OCT image level and volume level for the deep learning (DL) model for posterior vitreous detachment (PVD) detection. Area under the receiver operator characteristic curves (AUROCs) and average precision (AP) are depicted in the diagram. ROC = Receiving operator characteristic.