Characteristics of the Peripapillary Structure and Vasculature in Patients With Myopic Anisometropia

Techniques for imaging the choroid and choroidal blood flow in vivo

The choroid, which is a highly vascularized layer between the retina and sclera, is essential for supplying oxygen and nutrients to the outer retina. Choroidal vascular dysfunction has been implicated in numerous ocular diseases, including age-related macular degeneration, central serous chorioretinopathy, polypoidal choroidal vasculopathy, and myopia. Traditionally, the in vivo assessment of choroidal blood flow relies on techniques such as laser Doppler flowmetry, laser speckle flowgraphy, pneumotonometry, laser interferometry, and ultrasonic color Doppler imaging. While the aforementioned methods have provided valuable insights into choroidal blood flow regulation, their clinical applications have been limited. Recent advancements in optical coherence tomography and optical coherence tomography angiography have expanded our understanding of the choroid, allowing detailed visualization of the larger choroidal vessels and choriocapillaris, respectively. This review provides an overview of the available techniques that can investigate the choroid and its blood flow in vivo. Future research should combine these techniques to comprehensively image the entire choroidal microcirculation and develop robust methods to quantify choroidal blood flow. The potential findings will provide a better picture of choroidal hemodynamics and its effect on ocular health and disease.

Peripapillary choroidal vascularity of paediatric myopic eyes with peripapillary hyperreflective ovoid mass-like structures

PURPOSE

To assess the peripapillary choroidal vasculature in paediatric myopic patients with and without peripapillary hyperreflective ovoid mass-like structures (PHOMS).

METHODS

This prospective study includes 60 eyes of 60 myopic (spherical equivalent [SE] <-1.00 dioptre [D]) patients with (n = 30) and without (n = 30) PHOMS (PHOMS [+] and PHOMS [-] groups, respectively), and 30 eyes of 30 age- and sex-matched emmetropic children (control group). Peripapillary choroidal parameters, including total choroidal (TCA), luminal (LA), and stromal areas (SA) and choroidal vascularity index (CVI) calculated from vertical and horizontal single-line enhanced depth imaging-optical coherence tomography scans centred on optic nerve head.

RESULTS

Peripapillary retinal nerve fibre layer thicknesses were not different between the groups (p > 0.05). In the PHOMS (+) group, TCA, LA and SA were lower, and CVI was higher in all quadrants compared to the control (p < 0.05). However, only the mean TCA and LA in the inferior and nasal quadrants and the mean SA in the nasal quadrant were lower in PHOMS (+) than in PHOMS (-) (p < 0.05). In the PHOMS (-) group, higher CVI was observed in all quadrants except temporal compared to the control group. Although the mean CVI of the PHOMS (+) group was also higher than in the PHOMS (-) group, this difference was not statistically significant.

CONCLUSION

This study indicates that choroidal parameters differ in paediatric myopic patients with PHOMS. Further studies with larger sample sizes are needed to understand the details of choroidal parameters in eyes with PHOMS.

Development and validation of a deep learning model to predict axial length from ultra-wide field images

BACKGROUND

To validate the feasibility of building a deep learning model to predict axial length (AL) for moderate to high myopic patients from ultra-wide field (UWF) images.

METHODS

This study included 6174 UWF images from 3134 myopic patients during 2014 to 2020 in Eye and ENT Hospital of Fudan University. Of 6174 images, 4939 were used for training, 617 for validation, and 618 for testing. The coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) were used for model performance evaluation.

RESULTS

The model predicted AL with high accuracy. Evaluating performance of R2, MSE and MAE were 0.579, 1.419 and 0.9043, respectively. Prediction bias of 64.88% of the tests was under 1-mm error, 76.90% of tests was within the range of 5% error and 97.57% within 10% error. The prediction bias had a strong negative correlation with true AL values and showed significant difference between male and female (P < 0.001). Generated heatmaps demonstrated that the model focused on posterior atrophy changes in pathological fundus and peri-optic zone in normal fundus. In sex-specific models, R2, MSE, and MAE results of the female AL model were 0.411, 1.357, and 0.911 in female dataset and 0.343, 2.428, and 1.264 in male dataset. The corresponding metrics of male AL models were 0.216, 2.900, and 1.352 in male dataset and 0.083, 2.112, and 1.154 in female dataset.

CONCLUSIONS

It is feasible to utilize deep learning models to predict AL for moderate to high myopic patients with UWF images.