Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch’s membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma.

Spectralis optical coherence tomography (OCT) provided more detailed parameters in the peripapillary and macular areas among the OCT machines, but it is not easy to understand the enormous information (114 features) generated from Spectralis OCT in glaucoma assessment. Machine learning methodology has been well-applied in glaucoma detection in recent years and has the ability to process a large amount of information at once. Here we aimed to analyze the diagnostic capability of Spectralis OCT parameters on glaucoma detection using Support Vector Machine (SVM) classification method in our population. Our results showed that applying all OCT features with the SVM method had good capability in the detection of glaucomatous eyes (area under curve (AUC) = 0.82), as well as discriminating normal eyes from early, moderate, or severe glaucomatous eyes (AUC = 0.78, 0.89, and 0.93, respectively). Apart from using all OCT features, the minimum rim width (MRW) may be good feature groups to discriminate early glaucomatous from normal eyes (AUC = 0.78). The combination of peripapillary and macular parameters, including MRW_temporal inferior (TI), MRW_global (G), ganglion cell layer (GCL)_outer temporal (T2), GCL_inner inferior (I1), peripapillary nerve fiber layer thickness (ppNFLT)_temporal superior (TS), and GCL_inner temporal (T1), provided better results (AUC = 0.84). This study showed promise in glaucoma management in the Taiwanese population. However, further validation study is needed to test the performance of our proposed model in the real world.

Ophthalmic vascular occlusion is an infrequent but devastating complication following cosmetic facial filler injection. We report a case of developing retinal artery occlusion after poly-L-lactic acid (PLLA) injection. A 49-year-old woman with multiple chronic diseases experienced sudden central visual loss and severe ocular pain in the right eye immediately after PLLA injection in the temporal region. Her best-corrected visual acuity in the right eye dropped from 20/20 to 20/200. Fundus photography showed marked optic disc edema and localized retinal whitening in the territory of the blocked vessels. Spectral-domain optical coherence tomography revealed localized hyperreflectivity of the inner retina and retinal edema. Fluorescein angiography showed delayed filling of the retinal arteries and absence of retinal perfusion in the affected areas. Despite prompt aggressive management of the condition with ocular massage, topical brimonidine eyedrops, and hyperbaric oxygen therapy, the patient suffered permanent visual loss due to optic atrophy. Among all the subcutaneous filler materials, PLLA has not been a common cause of vascular complications, especially when injected in the temporal region, as this area has not been considered dangerous in the previous literature. Practitioners should be aware of the risk of visual loss, and extra care should be given on those who originally have a higher risk for vascular complications.

This review article provides comprehensive knowledge of the pathophysiology, treatment methods, prognosis, and prevention strategies of various kinds of iatrogenic ophthalmic artery occlusion. Especially in discussing the vascular complications after cosmetic facial filler injection, this review article covers the most cases, and has detailed analysis of the common injection sites and injected materials that cause complications. Clinicians can have complete concepts of the existing managements though this article in a short time.

OBJECTIVES: To evaluate the diagnostic performance of Spectralis optical coherence tomography (OCT) parameters for mild cognitive impairment (MCI) and mild dementia in an Asian population from Taiwan. METHODS: This retrospective cross-sectional study evaluated 43 patients with MCI (mean deviation [MD]: −5.05 ± 4.25 dB), 13 patients with mild dementia (MD: −9.03 ± 6.66 dB), and 32 healthy controls (MD: −2.50 ± 2.12 dB). The diagnostic sensitivity in identifying individuals with cognitive impairment of the Spectralis OCT parameters—such as those of the optic nerve head and macula—was compared across these groups. The area under the receiver operating characteristic curve (AUC) for each parameter was calculated to assess its sensitivity in differentiating between healthy eyes and those of individuals with MCI or mild dementia. RESULTS: Among the parameters evaluated, the Bruch’s membrane opening minimum rim width (BMO-MRW) nasal inferior region (ACU = 0.720) was the optimal parameter for distinguishing individuals with MCI from healthy controls. However, the highest AUC of 0.861 was achieved through a combination of five parameters. In distinguishing individuals with mild dementia from healthy controls, the BMO-MRW temporal superior region (ACU = 0.764) was the optimal parameter, with an AUC of 0.940 after adjusting for age and MD. Moreover, the condition of the macular nerve fiber layer outer inferior parameter moderately predicted disease progression (AUC = 0.713). CONCLUSIONS: Our real-world data demonstrate that Spectralis OCT measurements can detect MCI and mild dementia and predict disease progression.