Decreased retinal microvasculature densities in pterygium

Artificial intelligence assisted pterygium diagnosis: current status and perspectives

Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians with pterygium diagnosis. This paper provides an overview of AI-assisted pterygium diagnosis, including the AI techniques used such as machine learning, deep learning, and computer vision. Furthermore, recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection, classification and segmentation were summarized. The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed. The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis, which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.

Alterations in Macular Microvasculature in Pterygium Patients Measured by OCT Angiography

Previous studies have reported an association between pterygia and maculopathy, yet the underlying mechanisms and alterations to the macular microvasculature in pterygium patients have yet to be fully elucidated. Our study conducted an analysis of macular superficial vessel length density (VLD) and vessel perfusion density (VPD) to establish associations between the conjunctival and macular microvasculature in patients with unilateral and bilateral pterygia. We revealed a loss of macular microvasculature in the outer nasal (ON) region in both unilateral and bilateral pterygium patients. VLD was significantly decreased in both pterygium groups in the ON region, and VPD was notably lower in bilateral pterygium patients in the same area. Furthermore, in unilateral pterygium patients, the vessel percent pixel coverage (PPC) of the pterygium and the area of the pterygium exhibited a negative correlation with VLD in the ON region. Multiple stepwise linear regression models indicated that the PPC could best predict VLP in the ON region. Taken together, our findings suggest that patients with pterygia may be more susceptible to macular diseases, and this may be due to a compensatory increase in blood perfusion via the anterior ciliary artery. These results underscore the importance of managing maculopathy in patients with pterygia.

Nintedanib induces apoptosis in human pterygium cells through the FGFR2-ERK signalling pathway

AIM

To investigate whether nintedanib can inhibit pterygium cells through the fibroblast growth factor receptor 2 (FGFR2)/extracellular-signal-regulated kinase (ERK) pathway.

METHODS

Human primary pterygium cells were cultured in vitro. After treatment with nintedanib, the cell morphology was observed under microscopy, the morphological changes of the nucleus were observed after DAPI staining, apoptosis was analyzed by Annexin-V FITC/PI double staining, and the changes of apoptosis-associated proteins were detected by Western blot. The binding ability of nintedanib to FGFR2 was predicted by molecular docking. Finally, by silencing FGFR2, we explored whether nintedanib inhibited FGFR2/ERK pathway.

RESULTS

The results showed that nintedanib inhibited the growth of pterygium cells and caused nuclear pyknosis. The results of Annexin-VFITC/PI double staining showed that nintedanib was able to induce early and late apoptosis of pterygium cells, significantly increasing the expression of apoptosis-associated proteins Bax and cleaved-Caspase3 (P<0.05), and reducing the expression of Bcl-2 (P<0.05). In addition, nintedanib significantly inhibited ERK1/2 phosphorylation through FGFR2 (P<0.05). After silencing the expression of FGFR2, there was no significant difference in the inhibition of ERK1/2 phosphorylation by nintedanib (P>0.05).

CONCLUSION

Nintedanib induces apoptosis of pterygium cells by inhibiting FGFR2/ERK pathway.

Artificial intelligence-assisted diagnosis of ocular surface diseases

With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.

Decreased Macular Retinal Thickness in Patients With Pterygium

Purpose

To explore alterations in macular retinal thickness (RT) and analyze correlation between macular RT and pterygium area, length in pterygium patients.

Methods

Totally 13 patients with pterygium (left eye) and 13 healthy controls (left eye) were recruited. OCTA was applied to scan each eye to generate three-dimensional images. Based on the Early Treatment Diabetic Retinopathy Study (ETDRS) method, each image was divided into nine subregions for the ETDRS: central (C); inner superior (IS); outer superior (OS); inner nasal (IN); outer nasal (ON); inner inferior (II); outer inferior (OI); inner temporal (IT); and outer temporal (OT). The macular RT in each subregion was measured. Furthermore, the correlation between RT and the area, length of pterygium was analyzed.

Results

The visual acuity of pterygium patient was different from that of the control (P < 0.05). Besides, decreased intraretinal thickness of the IN and ON, increased intraretinal thickness of OT, decreased extraretinal thickness of OT, IN, ON, OS, and decreased retinal full layer thickness of medial superior, OS, IN, ON, and II subregions in pterygium group were observed. There was a negative correlation between RT of the IN and ON subregions and the length of pterygium (r = -0.5803 and r = -0.6013, P = 0.0376 and P = 0.0297). The RT of IN subregion was negatively correlated with pterygium area (r = -0.5844, P = 0.0359). According to the receiver operating characteristic analysis, in the ON subregion, the areas under the curve of the inner retinal thickness, outer retinal thickness and the whole retinal thickness were 1.0 (95% CI: 1.0), 0.882 (95% CI: 0.715 and 0.963), and 1.0 (95% CI: 1.0). The smallest area under the curve of retinal thickness in OT subregion was 0.018 (95% CI: 0-0.059).

Conclusion

RT of pterygium patients was significantly decreased, and the main alterations occurred in the temporal side suggesting there might exist retinal structural alterations in pterygium.