UTILIZATION OF SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY TO IDENTIFY POSTERIOR VITREOUS DETACHMENT IN PATIENTS WITH RETINAL DETACHMENT

ROLE OF OPTICAL COHERENCE TOMOGRAPHY IN MANAGEMENT OF ACUTE POSTERIOR VITREOUS DETACHMENT AND ITS COMPLICATIONS

PURPOSE

Currently, no consensus exists on the role of optical coherence tomography (OCT) imaging in the setting of acute posterior vitreous detachment (PVD). The authors outline the clinical utility of OCT in the management of acute PVD and its complications.

METHODS

Literature review of OCT findings in association with acute PVD and report of illustrative cases.

RESULTS

Optical coherence tomography imaging in the setting of acute PVD can provide details of vitreoretinal interface that are difficult to appreciate on biomicroscopy alone including partial PVDs, focal vitreoretinal adhesions and traction, and subclinical macular changes. The presence of vitreous hyperreflective dots on OCT in the premacular space, especially if severe, is highly correlated with the presence of peripheral retinal breaks and development of epiretinal membrane. Advancements in OCT technology, including enhanced vitreous imaging OCT, swept-source OCT, wide-angle OCT, and widefield OCT, allow for increased resolution and expanded field of imaging of the vitreoretinal interface.

CONCLUSION

Optical coherence tomography imaging is an emerging standard of care in the setting of patients presenting with new flashes and floaters. The authors highlight the benefits of OCT imaging in patients with acute PVD, which includes recognition of the status of the vitreoretinal interface, assistance in identifying high-risk PVDs, and performance of risk assessment that predict future macular pathologic condition.

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

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.