Optical Coherence Tomography Angiography Reveals Distinct Retinal Structural and Microvascular Abnormalities in Cerebrovascular Disease.

Cerebrovascular disease (CeVD) is one of the leading global causes of death and severe disability. To date, retinal microangiopathy has become a reflection of cerebral microangiopathy, mirroring the vascular pathological modifications in vivo. To evaluate the retinal structure and microvasculature in patients with CeVD, we conducted a cross-sectional study in Zhongshan Ophthalmic Center and Department of Neurology of Third Affiliated Hospital, Sun Yat-sen University using optical coherence tomography angiography (OCTA). CeVD patients (n = 121; 238 eyes) and healthy controls (n = 44; 57 eyes) were included in the analysis. The CeVD group showed significant thinning of the peripapillary retinal nerve fiber layer (pRNFL) thickness in the temporal and nasal quadrants, and thinning of the macular ganglion cell-inner plexiform layer (GC-IPL) in the inferior quadrant, while macular microvasculature reduction was prominent in all nine quadrants. There were significant correlations between OCTA parameters, visual acuity, and transcranial doppler parameters in the CeVD group. The specific structural parameters combining microvasculature indices showed the best diagnostic accuracies (AUC = 0.918) to discriminate CeVD group from healthy controls. To conclude, we proved that OCTA reveals specific patterns of retinal structural changes and extensive macular microvascular changes in CeVD. Additionally, these retinal abnormalities could prove useful disease biomarkers in the management of individuals at high risk of debilitating complications from a cerebrovascular event

Comparison of macular structural and vascular changes in neuromyelitis optica spectrum disorder and primary open angle glaucoma: a cross-sectional study.

AIMS: To compare macular structure and vasculature between neuromyelitis optica spectrum disorder (NMOSD) and primary open angle glaucoma (POAG) using optical coherence tomography angiography. METHODS: NMOSD patients (n=124) with/without a history of optic neuritis (ON) (NMO+ON: 113 eyes; NMO-ON: 95 eyes), glaucomatous patients (n=102) with early/advanced glaucoma (G-E: 74 eyes; G-A: 50 eyes) and healthy controls (n=62; 90 eyes) were imaged. The main outcome measures were macular ganglion cell-inner plexiform layer (GC-IPL) thickness, vessel density (VD) and perfusion density (PD) in the superficial capillary plexus, and diagnostic capabilities of the parameters as calculated by area under the curve (AUC). RESULTS: Significant losses in GC-IPL, VD and PD were detected in both patients with NMOSD and POAG. With matched losses in the peripapillary retinal nerve fibre layer, NMOSD group showed significant thinning of GC-IPL in the nasal-superior quadrant, whereas in POAG group, significant thinning was observed in the inferior and temporal-inferior quadrants. GC-IPL thinning was more prominent in the superior, nasal-superior and nasal-inferior quadrants in NMO+ON eyes. In G-A eyes, significant GC-IPL thinning was seen in the temporal-inferior quadrant. The specific structural parameters combining VD and foveal avascular zone (FAZ) indices showed the best diagnostic accuracies. The FAZ area in eyes with NMOSD was significantly smaller than the eyes of healthy controls and POAG. CONCLUSION: NMOSD and POAG have specific patterns of macular structural and vascular changes associated with pathophysiology. Our results indicate that FAZ could be a sensitive biomarker of macular changes in NMOSD

Postoperative myopic shift and visual acuity rehabilitation in patients with bilateral congenital cataracts

BackgroundThis study aimed to explore the postoperative myopic shift and its relationship to visual acuity rehabilitation in patients with bilateral congenital cataracts (CCs).MethodsBilateral CC patients who underwent cataract extraction and primary intraocular lens implantations before 6 years old were included and divided into five groups according to surgical ages (<2, 2–3, 3–4, 4–5, and 5–6 years). The postoperative myopic shift rates, spherical equivalents (SEs), and the best corrected visual acuity (BCVA) were measured and analyzed.ResultsA total of 1,137 refractive measurements from 234 patients were included, with a mean follow-up period of 34 months. The postoperative mean SEs at each follow-up in the five groups were linearly fitted with a mean R2 = 0.93 ± 0.03, which showed a downtrend of SE with age (linear regression). Among patients with a follow-up of 4 years, the mean postoperative myopic shift rate was 0.84, 0.81, 0.68, 0.24, and 0.28 diopters per year (D/y) in the five age groups (from young to old), respectively. The BCVA of those with a surgical age of <2 years at the 4-year visit was 0.26 (LogMAR), and the mean postoperative myopic shift rate was 0.84 D/y. For patients with a surgical age of 2–6 years, a poorer BCVA at the 4-year visit was found in those with higher postoperative myopic shift rates (r = 0.974, p = 0.026, Pearson’s correlation test).ConclusionPerforming cataract surgery for patients before 2 years old and decreasing the postoperative myopic shift rates for those with a surgical age of 2–6 years may benefit visual acuity rehabilitation

A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal images

Chronic kidney disease (CKD) is a global health challenge, but invasive renal biopsies, the gold standard for diagnosis and prognosis, are often clinically constrained. To address this, we developed the kidney intelligent diagnosis system (KIDS), a noninvasive model for renal biopsy prediction using 13,144 retinal images from 6773 participants. The KIDS achieves an area under the receiver operating characteristic curve (AUC) of 0.839–0.993 for CKD screening and accurately identifies the five most common pathological types (AUC: 0.790–0.932) in a multicenter and multi-ethnic validation, outperforming nephrologists by 26.98% in accuracy. Additionally, the KIDS further predicts disease progression based on pathological classification. Given its flexible strategy, the KIDS can be adapted to local conditions to provide a tailored tool for patients. This noninvasive model has the potential to improve CKD clinical management, particularly for those who are ineligible for biopsies

Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review

Deep learning (DL) is the new high-profile technology in medical artificial intelligence (AI) for building screening and diagnosing algorithms for various diseases. The eye provides a window for observing neurovascular pathophysiological changes. Previous studies have proposed that ocular manifestations indicate systemic conditions, revealing a new route in disease screening and management. There have been multiple DL models developed for identifying systemic diseases based on ocular data. However, the methods and results varied immensely across studies. This systematic review aims to summarize the existing studies and provide an overview of the present and future aspects of DL-based algorithms for screening systemic diseases based on ophthalmic examinations. We performed a thorough search in PubMed®, Embase, and Web of Science for English-language articles published until August 2022. Among the 2873 articles collected, 62 were included for analysis and quality assessment. The selected studies mainly utilized eye appearance, retinal data, and eye movements as model input and covered a wide range of systemic diseases such as cardiovascular diseases, neurodegenerative diseases, and systemic health features. Despite the decent performance reported, most models lack disease specificity and public generalizability for real-world application. This review concludes the pros and cons and discusses the prospect of implementing AI based on ocular data in real-world clinical scenarios.</jats:p

Application of Surgical Decision Model for Patients With Childhood Cataract: A Study Based on Real World Data

Reliable validated methods are necessary to verify the performance of diagnosis and therapy-assisted models in clinical practice. However, some validated results have research bias and may not reflect the results of real-world application. In addition, the conduct of clinical trials has executive risks for the indeterminate effectiveness of models and it is challenging to finish validated clinical trials of rare diseases. Real world data (RWD) can probably solve this problem. In our study, we collected RWD from 251 patients with a rare disease, childhood cataract (CC) and conducted a retrospective study to validate the CC surgical decision model. The consistency of the real surgical type and recommended surgical type was 94.16%. In the cataract extraction (CE) group, the model recommended the same surgical type for 84.48% of eyes, but the model advised conducting cataract extraction and primary intraocular lens implantation (CE + IOL) surgery in 15.52% of eyes, which was different from the real-world choices. In the CE + IOL group, the model recommended the same surgical type for 100% of eyes. The real-recommended matched rates were 94.22% in the eyes of bilateral patients and 90.38% in the eyes of unilateral patients. Our study is the first to apply RWD to complete a retrospective study evaluating a clinical model, and the results indicate the availability and feasibility of applying RWD in model validation and serve guidance for intelligent model evaluation for rare diseases.</jats:p