Automatic identification and characterization of the epiretinal membrane in OCT images

The Role of Artificial Intelligence in Epiretinal Membrane Care: A Scoping Review

Topic

In ophthalmology, artificial intelligence (AI) demonstrates potential in using ophthalmic imaging across diverse diseases, often matching ophthalmologists' performance. However, the range of machine learning models for epiretinal membrane (ERM) management, which differ in methodology, application, and performance, remains largely unsynthesized.

Clinical Relevance

Epiretinal membrane management relies on clinical evaluation and imaging, with surgical intervention considered in cases of significant impairment. AI analysis of ophthalmic images and clinical features could enhance ERM detection, characterization, and prognostication, potentially improving clinical decision-making. This scoping review aims to evaluate the methodologies, applications, and reported performance of AI models in ERM diagnosis, characterization, and prognostication.

Methods

A comprehensive literature search was conducted across 5 electronic databases including Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science Core Collection from inception to November 14, 2024. Studies pertaining to AI algorithms in the context of ERM were included. The primary outcomes measured will be the reported design, application in ERM management, and performance of each AI model.

Results

Three hundred ninety articles were retrieved, with 33 studies meeting inclusion criteria. There were 30 studies (91%) reporting their training and validation methods. Altogether, 61 distinct AI models were included. OCT scans and fundus photographs were used in 26 (79%) and 7 (21%) papers, respectively. Supervised learning and both supervised and unsupervised learning were used in 32 (97%) and 1 (3%) studies, respectively. Twenty-seven studies (82%) developed or adapted AI models using images, whereas 5 (15%) had models using both images and clinical features, and 1 (3%) used preoperative and postoperative clinical features without ophthalmic images. Study objectives were categorized into 3 stages of ERM care. Twenty-three studies (70%) implemented AI for diagnosis (stage 1), 1 (3%) identified ERM characteristics (stage 2), and 6 (18%) predicted vision impairment after diagnosis or postoperative vision outcomes (stage 3). No articles studied treatment planning. Three studies (9%) used AI in stages 1 and 2. Of the 16 studies comparing AI performance to human graders (i.e., retinal specialists, general ophthalmologists, and trainees), 10 (63%) reported equivalent or higher performance.

Conclusion

Artificial intelligence-driven assessments of ophthalmic images and clinical features demonstrated high performance in detecting ERM, identifying its morphological properties, and predicting visual outcomes following ERM surgery. Future research might consider the validation of algorithms for clinical applications in personal treatment plan development, ideally to identify patients who might benefit most from surgery.

Financial Disclosures

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Mechanical impact of epiretinal membranes on the retina utilizing finite element analysis

BACKGROUND AND OBJECTIVE

Epiretinal membrane (ERM) is a transparent membrane that forms on the surface of the neurosensory retina, causing tangential traction on the retinal surface, which may contribute to cell proliferation and contraction. Epiretinal membranes (ERMs) may be asymptomatic in some patients, while in others the membranes can progress, resulting in macular thickening and macular traction, thus distorting and inducing loss of central visual function and metamorphopsia. Currently, treatment options include follow-up or pars plana vitrectomy with an ERM peel, aiming to relieve the macular traction and improve vision and metamorphopsia. No specific criteria exist for predicting which patients might progress and need early surgery to improve and maintain good vision. The decision for surgery is based on the individual's symptoms and the physician's judgment. This study aimed to evaluate the mechanical impact in terms of stress and deformations of the ERM and to qualitatively compare them with the clinical progression of fovea thickening observed through optical coherence tomography (OCT) images.

METHODS

Numerical simulation on a three-dimensional geometrical retina and ERM model was applied to isolate factors that can be used to predict its progression and prognosis. OCT images of 14 patients with ERM were used to derive the fovea thickness progression before and after vitrectomy surgery with ERM peeling.

RESULTS

The results clearly show that the increase in ERM contractility level increases the developed stress at the fovea, which spreads and advances toward its base. The highest stress level (2.1 kPa) was developed at the highest and asymmetric contractility, producing non-uniform distributed deformations that distort the fovea structure.

CONCLUSIONS

These findings imply that high and asymmetric ERM contractility should be evaluated clinically as a factor that might signal the need for early vitrectomy surgery to avoid irreversible visual loss. Moreover, the OCT images revealed that in some cases, the thickness of the fovea indeed remains high, even after ∼12 months postoperatively, which also indicates that the deformation of the fovea in these cases is irreversible.

Fully automatic segmentation and monitoring of choriocapillaris flow voids in OCTA images

Optical coherence tomography angiography (OCTA) is a non-invasive ophthalmic imaging modality that is widely used in clinical practice. Recent technological advances in OCTA allow imaging of blood flow deeper than the retinal layers, at the level of the choriocapillaris (CC), where a granular image is obtained showing a pattern of bright areas, representing blood flow, and a pattern of small dark regions, called flow voids (FVs). Several clinical studies have reported a close correlation between abnormal FVs distribution and multiple diseases, so quantifying changes in FVs distribution in CC has become an area of interest for many clinicians. However, CC OCTA images present very complex features that make it difficult to correctly compare FVs during the monitoring of a patient. In this work, we propose fully automatic approaches for the segmentation and monitoring of FVs in CC OCTA images. First, a baseline approach, in which a fully automatic segmentation methodology based on local contrast enhancement and global thresholding is proposed to segment FVs and measure changes in their distribution in a straightforward manner. Second, a robust approach in which, prior to the use of our segmentation methodology, an unsupervised trained neural network is used to perform a deformable registration that aligns inconsistencies between images acquired at different time instants. The proposed approaches were tested with CC OCTA images collected during a clinical study on the response to photodynamic therapy in patients affected by chronic central serous chorioretinopathy (CSC), demonstrating their clinical utility. The results showed that both approaches are accurate and robust, surpassing the state of the art, therefore improving the efficacy of FVs as a biomarker to monitor the patient treatments. This gives great potential for the clinical use of our methods, with the possibility of extending their use to other pathologies or treatments associated with this type of imaging.

Multicolor image classification using the multimodal information bottleneck network (MMIB-Net) for detecting diabetic retinopathy

Multicolor (MC) imaging is an imaging modality that records confocal scanning laser ophthalmoscope (cSLO) fundus images, which can be used for the diabetic retinopathy (DR) detection. By utilizing this imaging technique, multiple modal images can be obtained in a single case. Additional symptomatic features can be obtained if these images are considered during the diagnosis of DR. However, few studies have been carried out to classify MC Images using deep learning methods, let alone using multi modal features for analysis. In this work, we propose a novel model which uses the multimodal information bottleneck network (MMIB-Net) to classify the MC Images for the detection of DR. Our model can extract the features of multiple modalities simultaneously while finding concise feature representations of each modality using the information bottleneck theory. MC Images classification can be achieved by picking up the combined representations and features of all modalities. In our experiments, it is shown that the proposed method can achieve an accurate classification of MC Images. Comparative experiments also demonstrate that the use of multimodality and information bottleneck improves the performance of MC Images classification. To the best of our knowledge, this is the first report of DR identification utilizing the multimodal information bottleneck convolutional neural network in MC Images.

Optimizing Visualization of Membranes in Macular Surgery With Heads-Up Display

BACKGROUND AND OBJECTIVE

To determine which optical parameter profiles (OPPs) can be utilized to improve the visualization of epiretinal membranes (ERMs) and the internal limiting membrane (ILM) using a three-dimensional heads-up microscope during 25-gauge pars plana vitrectomy.

PATIENTS AND METHODS

Fourteen independent graders were asked to complete a questionnaire comparing each of the OPPs against the unaltered control image for each given surgical case.

RESULTS

Analysis of the graders' responses indicated that higher values of hue are correlated with better visualization of ERM/ILM before and after dye application. There was overall agreement that OPPs could be used to enhance the visualization of the ERM and ILM during surgery.

CONCLUSIONS

The use of OPPs to improve the visualization of specific structures is still new and heavily dependent on surgeon preference. The authors' study shows that some OPPs may enhance the visualization of the ERM and ILM during macular surgery. [Ophthalmic Surg Lasers Imaging Retina. 2020;51:584-587.].

Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes

Optical Coherence Tomography (OCT) is a medical image modality providing high-resolution cross-sectional visualizations of the retinal tissues without any invasive procedure, commonly used in the analysis of retinal diseases such as diabetic retinopathy or retinal detachment. Early identification of the epiretinal membrane (ERM) facilitates ERM surgical removal operations. Moreover, presence of the ERM is linked to other retinal pathologies, such as macular edemas, being among the main causes of vision loss. In this work, we propose an automatic method for the characterization and visualization of the ERM's presence using 3D OCT volumes. A set of 452 features is refined using the Spatial Uniform ReliefF (SURF) selection strategy to identify the most relevant ones. Afterwards, a set of representative classifiers is trained, selecting the most proficient model, generating a 2D reconstruction of the ERM's presence. Finally, a post-processing stage using a set of morphological operators is performed to improve the quality of the generated maps. To verify the proposed methodology, we used 20 3D OCT volumes, both with and without the ERM's presence, totalling 2428 OCT images manually labeled by a specialist. The most optimal classifier in the training stage achieved a mean accuracy of 91 . 9 % . Regarding the post-processing stage, mean specificity values of 91 . 9 % and 99 . 0 % were obtained from volumes with and without the ERM's presence, respectively.

Automatic Identification and Representation of the Cornea-Contact Lens Relationship Using AS-OCT Images

The clinical study of the cornea-contact lens relationship is widely used in the process of adaptation of the scleral contact lens (SCL) to the ocular morphology of patients. In that sense, the measurement of the adjustment between the SCL and the cornea can be used to study the comfort or potential damage that the lens may produce in the eye. The current analysis procedure implies the manual inspection of optical coherence tomography of the anterior segment images (AS-OCT) by the clinical experts. This process presents several limitations such as the inability to obtain complex metrics, the inaccuracies of the manual measurements or the requirement of a time-consuming process by the expert in a tedious process, among others. This work proposes a fully-automatic methodology for the extraction of the areas of interest in the study of the cornea-contact lens relationship and the measurement of representative metrics that allow the clinicians to measure quantitatively the adjustment between the lens and the eye. In particular, three distance metrics are herein proposed: Vertical, normal to the tangent of the region of interest and by the nearest point. Moreover, the images are classified to characterize the analysis as belonging to the central cornea, peripheral cornea, limbus or sclera (regions where the inner layer of the lens has already joined the cornea). Finally, the methodology graphically presents the results of the identified segmentations using an intuitive visualization that facilitates the analysis and diagnosis of the patients by the clinical experts.