Automated Analysis of Choroidal Sublayer Morphologic Features in Myopic Children Using EDI-OCT by Deep Learning

Choroidal Optical Coherence Tomography Angiography: Noninvasive Choroidal Vessel Analysis via Deep Learning

Background: The choroid is the most vascularized structure in the human eye, associated with numerous retinal and choroidal diseases. However, the vessel distribution of choroidal sublayers has yet to be effectively explored due to the lack of suitable tools for visualization and analysis. Methods: In this paper, we present a novel choroidal angiography strategy to more effectively evaluate vessels within choroidal sublayers in the clinic. Our approach utilizes a segmentation model to extract choroidal vessels from OCT B-scans layer by layer. Furthermore, we ensure that the model, trained on B-scans with high choroidal quality, can proficiently handle the low-quality B-scans commonly collected in clinical practice for reconstruction vessel distributions. By treating this process as a cross-domain segmentation task, we propose an ensemble discriminative mean teacher structure to address the specificities inherent in this cross-domain segmentation process. The proposed structure can select representative samples with minimal label noise for self-training and enhance the adaptation strength of adversarial training. Results: Experiments demonstrate the effectiveness of the proposed structure, achieving a dice score of 77.28 for choroidal vessel segmentation. This validates our strategy to provide satisfactory choroidal angiography noninvasively, supportting the analysis of choroidal vessel distribution for paitients with choroidal diseases. We observed that patients with central serous chorioretinopathy have evidently (P < 0.05) lower vascular indexes at all choroidal sublayers than healthy individuals, especially in the region beyond central fovea of macula (larger than 6 mm). Conclusions: We release the code and training set of the proposed method as the first noninvasive mechnism to assist clinical application for the analysis of choroidal vessels.

Choroidal Layer Analysis in OCT images via Ambiguous Boundary-aware Attention

Optical Coherence Tomography (OCT) is a commonly used retina imaging technique, and it is capable of revealing the morphology of the choroid. However, the segmentation and quantitative analysis of the sublayers and vessels in choroid are rarely explored, primarily due to the indistinct boundaries of choroidal sublayers, and imbalanced distribution of vessels observed in OCT imagery. In this paper, we propose a novel two-stage architecture called Choroidal Layer Analysis network (CLA), that may be considered the first attempt in this research community for joint segmentation of choroidal sublayers and choroidal vessels in OCT images. CLA employs the encoder-decoder network with the residual U-shape module as the backbone. In order to empower the ability of the segmentation model to identify the inconspicuous boundaries of choroidal sublayers, we introduce an Ambiguous Boundary Attention block (ABA) into the bottleneck of the encoder-decoder network in the first stage. For more accurate segmentation of large choroidal vessels with ambiguous contours and imbalanced spatial distribution, the second stage introduces an active contour-based loss to refine the contours of choroidal vessels simultaneously with precise identification of each vessel via contextual modeling. To train, test and validate the proposed model, we conducted a choroidal segmentation dataset containing 800 OCT images, with their sublayers and large choroidal vessels manually annotated. Experimental results demonstrate the superiority of the proposed approach compared with other state-of-the-art segmentation networks in large margins. It is worth noting that we also reconstructed the large choroidal vessels in three-dimensional (3D) based on the segmentation results, and multiple 3D morphological parameters were calculated. The statistical analysis of these parameters demonstrates significant differences between the healthy control and high myopia group, and this further confirms the proposed work may facilitate subsequent disease understanding and clinical decision-making.

Effect of spectacle lenses with aspherical lenslets on choroidal thickness in myopic children: a 3-year follow-up study

BACKGROUND

To investigate the impact of wearing spectacle lenses with highly aspherical lenslets (HAL) for 3 years and the impact of switching from single-vision lenses (SVL) to HAL on choroidal thickness (ChT).

METHODS

Fifty-one participants who had already worn HAL for 2 years continued wearing them for an additional year (HAL group). Further, 50 and 41 participants who had worn spectacle lenses with slightly aspherical lenslets (SAL) and SVL for 2 years, respectively, switched to wearing HAL for another year (SAL-HAL and SVL-HAL groups). Additionally, 48 new participants aged 10-15 years were enrolled to wear SVL at the third year (new-SVL group). ChT was measured every 6 months throughout the study.

RESULTS

Significant differences were observed in the changes in ChT among the four groups at the third year (all P < 0.05 except for the outer nasal region: P = 0.09), with the new-SVL group showing larger reductions compared with the other three groups. However, none of the three HAL-wearing groups showed significant changes in ChT at the third year (all P > 0.05). When comparing the changes in ChT for 3 years among the HAL, SAL-HAL, and SVL-HAL groups, significant differences were found before switching to HAL, but these differences were abolished after all participants switched to HAL.

CONCLUSIONS

Compared to those in the SVL group, choroid thinning was significantly inhibited in all the HAL groups. Wearing HAL for 3 years no longer had a choroidal thickening effect but could still inhibit choroidal thinning compared to wearing SVL.

TRIAL REGISTRATION

The study was registered at the Chinese Clinical Trial Registry (ChiCTR1800017683), http://www.chictr.org.cn/showproj.aspx?proj=29789 .

Choroidal Vascularity Index and Choroidal Structural Changes in Children With Nephrotic Syndrome

Purpose

To investigate the choroidal vascularity index (CVI) and choroidal structural changes in children with nephrotic syndrome.

Methods

This was a cross-sectional study involving 45 children with primary nephrotic syndrome and 40 normal controls. All participants underwent enhanced depth imaging-optical coherence tomography examinations. An automatic segmentation method based on deep learning was used to segment the choroidal vessels and stroma, and the choroidal volume (CV), vascular volume (VV), and CVI within a 4.5 mm diameter circular area centered around the macular fovea were obtained. Clinical data, including blood lipids, serum proteins, renal function, and renal injury indicators, were collected from the patients.

Results

Compared with normal controls, children with nephrotic syndrome had a significant increase in CV (nephrotic syndrome: 4.132 ± 0.464 vs. normal controls: 3.873 ± 0.574; P = 0.024); no significant change in VV (nephrotic syndrome: 1.276 ± 0.173 vs. normal controls: 1.277 ± 0.165; P = 0.971); and a significant decrease in the CVI (nephrotic syndrome: 0.308 [range, 0.270-0.386] vs. normal controls: 0.330 [range, 0.288-0.387]; P < 0.001). In the correlation analysis, the CVI was positively correlated with serum total protein, serum albumin, serum prealbumin, ratio of serum albumin to globulin, and 24-hour urine volume and was negatively correlated with total cholesterol, low-density lipoprotein cholesterol, urinary protein concentration, and ratio of urinary transferrin to creatinine (all P < 0.05).

Conclusions

The CVI is significantly reduced in children with nephrotic syndrome, and the decrease in the CVI parallels the severity of kidney disease, indicating choroidal involvement in the process of nephrotic syndrome.

Translational Relevance

Our findings contribute to a deeper understanding of how nephrotic syndrome affects the choroid.

Insights into artificial intelligence in myopia management: from a data perspective

Given the high incidence and prevalence of myopia, the current healthcare system is struggling to handle the task of myopia management, which is worsened by home quarantine during the ongoing COVID-19 pandemic. The utilization of artificial intelligence (AI) in ophthalmology is thriving, yet not enough in myopia. AI can serve as a solution for the myopia pandemic, with application potential in early identification, risk stratification, progression prediction, and timely intervention. The datasets used for developing AI models are the foundation and determine the upper limit of performance. Data generated from clinical practice in managing myopia can be categorized into clinical data and imaging data, and different AI methods can be used for analysis. In this review, we comprehensively review the current application status of AI in myopia with an emphasis on data modalities used for developing AI models. We propose that establishing large public datasets with high quality, enhancing the model's capability of handling multimodal input, and exploring novel data modalities could be of great significance for the further application of AI for myopia.

Correlation between refractive errors and ocular biometric parameters in children and adolescents: a systematic review and meta-analysis

BACKGROUND

Refractive errors are one of the most common ocular conditions among children and adolescents, with myopia showing an increasing prevalence and early onset in this population. Recent studies have identified a correlation between refractive errors and ocular biometric parameters.

METHODS

A systematic search was conducted in electronic databases including PubMed, EMBASE, Cochrane Library, Web of Science, and Medline from January 1, 2012, to May 1, 2023. Various ocular biometric parameters were summarized under different refractive states, including axial length (AL), central corneal thickness (CCT), anterior chamber depth (ACD), lens thickness (LT), corneal curvature (CC), Corneal curvature radius (CR),axial length-to-corneal radius ratio (AL/CR ratio), choroidal thickness (ChT), retinal thickness (RT), retinal nerve fiber layer thickness (RNFL), and retinal blood density (VD). The differences in these parameters among different refractive states were analyzed using Stata software with fixed or random-effects models, taking into account the assessed heterogeneity level.

RESULTS

This meta-analysis included a total of 69 studies involving 128,178 eyes, including 48,795 emmetropic eyes, 60,691 myopic eyes, 13,983 hyperopic eyes, 2,040 low myopic eyes, 1,201 moderate myopic eyes, and 1,468 high myopic eyes. The results of our study demonstrated that, compared to the control group (emmetropic group), the myopic group and low, moderate, and high myopic groups showed significant increases in AL, AL/CR ratio, and ACD, while the hyperopic group exhibited significant decreases. Compared to the control group, the myopic group had a significantly increase for CC, while CR, CCT, perifoveal RT, subfoveal ChT, foveal ChT, parafoveal ChT, perifoveal (except nasal) ChT, and pRNFL (except temporal) significantly decreased. Compared to the control group, the hyperopic group had a significantly increase for subfoveal ChT, foveal ChT, parafoveal ChT, perifoveal ChT, and nasal pRNFL. Compared to the control group, the low and moderate myopic groups had a significantly decreases for the CCT, parafoveal RT (except nasal), perifoveal RT (except nasal), and pRNFL (except superior and temporal). Compared to the control group, the high myopic group had a significantly increase for CR, while LT, perifoveal ChT (except nasal), parafoveal RT, perifoveal RT, and pRNFL (except temporal) had significant decreased.

CONCLUSION

The changes of ocular biometric parameters in children and adolescents are closely related to refractive errors. Ocular biometric parameters devices, as effective non-invasive techniques, provide objective biological markers for monitoring refractive errors such as myopia.

Comparison of the morphological characteristics of the choroidal sublayer between idiopathic macular holes and epiretinal membranes with automatic analysis

PURPOSE

To compare the choroidal sublayer morphologic features between idiopathic macular hole (IMH) and idiopathic epiretinal membrane (iERM) on spectral-domain optical coherent tomography (SD-OCT) using an automatic segmentation model.

METHODS

Thirty-three patients with idiopathic IMHs and 44 with iERMs who underwent vitrectomies were involved. The enhanced depth imaging mode of SD-OCT was used to obtain the B-scan image after single line scanning of the macular fovea. The choroidal sublayer automatic analysis model divides the choroidal into the choroidal large vessel layer, the middle vessel layer and the small vessel layer (LVCL, MVCL and SVCL, respectively) and calculates the choroidal thickness (overall, LVCL, MVCL and SVCL) and vascular index (overall, LVCL, MVCL and SVCL). The morphological characteristics of the choroidal sublayer in the ERM eyes and the IMH eyes were compared.

RESULTS

The mean choroidal thickness in the macular centre of the IMH eyes was significantly thinner than that of the ERM eyes (206.35 ± 81.72 vs. 273.33 ± 82.31 μm; P < 0.001). The analysis of the choroidal sublayer showed that the MVCL and SVCL macular centres and 0.5-1.5 mm of the nasal and temporal macula were significantly thinner in the IMH eyes than in the ERM eyes (P < 0.05), and there was a difference in the macular centre of the LVCL between the two groups (P < 0.05). In contrast, the choroidal vascular index of the macular centre in the IMH eyes was significantly higher than that in iERM eyes (0.2480 ± 0.0536 vs. 0.2120 ± 0.0616; P < 0.05). There was no significant difference in the CVI of other parts of the macula, the LVCL or MVCL between the two groups.

CONCLUSION

The choroidal thickness of the IMH eyes was significantly thinner than that of the iERM eyes, which was mainly observed in 3 mm of the macular centre and the MVCL and SVCL layers of the choroid. The choroidal vascular index of the IMH eyes was higher than that of the iERM eyes. These findings suggest that the choroid may be involved in the pathogenesis of IMH and iERM.

A Deep Learning-Based Fully Automated Program for Choroidal Structure Analysis Within the Region of Interest in Myopic Children

Purpose

To develop and validate a fully automated program for choroidal structure analysis within a 1500-µm-wide region of interest centered on the fovea (deep learning-based choroidal structure assessment program [DCAP]).

Methods

A total of 2162 fovea-centered radial swept-source optical coherence tomography (SS-OCT) B-scans from 162 myopic children with cycloplegic spherical equivalent refraction ranging from -1.00 to -5.00 diopters were collected to develop the DCAP. Medical Transformer network and Small Attention U-Net were used to automatically segment the choroid boundaries and the nulla (the deepest point within the fovea). Automatic denoising based on choroidal vessel luminance and binarization were applied to isolate choroidal luminal/stromal areas. To further compare the DCAP with the traditional handcrafted method, the luminal/stromal areas and choroidal vascularity index (CVI) values for 20 OCT images were measured by three graders and the DCAP separately. Intraclass correlation coefficients (ICCs) and limits of agreement were used for agreement analysis.

Results

The mean ± SD pixel-wise distances from the predicted choroidal inner, outer boundary, and nulla to the ground truth were 1.40 ± 1.23, 5.40 ± 2.24, and 1.92 ± 1.13 pixels, respectively. The mean times required for choroidal structure analysis were 1.00, 438.00 ± 75.88, 393.25 ± 78.77, and 410.10 ± 56.03 seconds per image for the DCAP and three graders, respectively. Agreement between the automatic and manual area measurements was excellent (ICCs > 0.900) but poor for the CVI (0.627; 95% confidence interval, 0.279-0.832). Additionally, the DCAP demonstrated better intersession repeatability.

Conclusions

The DCAP is faster than manual methods. Also, it was able to reduce the intra-/intergrader and intersession variations to a small extent.

Translational Relevance

The DCAP could aid in choroidal structure assessment.

Artificial intelligence technology for myopia challenges: A review

Myopia is a significant global health concern and affects human visual function, resulting in blurred vision at a distance. There are still many unsolved challenges in this field that require the help of new technologies. Currently, artificial intelligence (AI) technology is dominating medical image and data analysis and has been introduced to address challenges in the clinical practice of many ocular diseases. AI research in myopia is still in its early stages. Understanding the strengths and limitations of each AI method in specific tasks of myopia could be of great value and might help us to choose appropriate approaches for different tasks. This article reviews and elaborates on the technical details of AI methods applied for myopia risk prediction, screening and diagnosis, pathogenesis, and treatment.

Advances in OCT Imaging in Myopia and Pathologic Myopia

Advances in imaging with optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) technology, including the development of swept source OCT/OCTA, widefield or ultra-widefield systems, have greatly improved the understanding, diagnosis, and treatment of myopia and myopia-related complications. Anterior segment OCT is useful for imaging the anterior segment of myopes, providing the basis for implantable collamer lens optimization, or detecting intraocular lens decentration in high myopic patients. OCT has enhanced imaging of vitreous properties, and measurement of choroidal thickness in myopic eyes. Widefield OCT systems have greatly improved the visualization of peripheral retinal lesions and have enabled the evaluation of wide staphyloma and ocular curvature. Based on OCT imaging, a new classification system and guidelines for the management of myopic traction maculopathy have been proposed; different dome-shaped macula morphologies have been described; and myopia-related abnormalities in the optic nerve and peripapillary region have been demonstrated. OCTA can quantitatively evaluate the retinal microvasculature and choriocapillaris, which is useful for the early detection of myopic choroidal neovascularization and the evaluation of anti-vascular endothelial growth factor therapy in these patients. In addition, the application of artificial intelligence in OCT/OCTA imaging in myopia has achieved promising results.