International Journal of Retina and Vitreous最新文献

Background: Diabetic retinopathy (DR) stands as the foremost cause of preventable blindness in adults. Despite efforts to expand DR screening coverage in the Brazilian public healthcare system, challenges persist due to various factors including social, medical, and financial constraints. Our objective was to evaluate the quality of images obtained with the AirDoc, a novel device, compared to Eyer portable camera which has already been clinically validated.

Methods: Images were captured by two portable retinal devices: AirDoc and Eyer. The included patients had their fundus images obtained in a screening program conducted in Blumenau, Santa Catarina. Two retina specialists independently assessed image's quality. A comparison was performed between both devices regarding image quality and the presence of artifacts.

Results: The analysis included 129 patients (mean age of 61 years), with 29 (43.28%) male and an average disease duration of 11.1 ± 8 years. In Ardoc, 21 (16.28%) images were classified as poor quality, with 88 (68%) presenting artifacts; in Eyer, 4 (3.1%) images were classified as poor quality, with 94 (72.87%) presenting artifacts.

Conclusions: Although both Eyer and AirDoc devices show potential as screening tools, the AirDoc images displayed higher rates of ungradable and low-quality images, that may directly affect the DR and DME grading. We must acknowledge the limitations of our study, including the relatively small sample size. Therefore, the interpretations of our analyses should be approached with caution, and further investigations with larger patient cohorts are warranted to validate our findings.

Aim: To adopt a novel artificial intelligence (AI) optical coherence tomography (OCT)-based program to identify the presence of biomarkers associated with central serous chorioretinopathy (CSC) and whether these can differentiate between acute and chronic central serous chorioretinopathy (aCSC and cCSC).

Methods: Multicenter, observational study with a retrospective design enrolling treatment-naïve patients with aCSC and cCSC. The diagnosis of aCSC and cCSC was established with multimodal imaging and for the current study subsequent follow-up visits were also considered. Baseline OCTs were analyzed by an AI-based platform (Discovery® OCT Fluid and Biomarker Detector, RetinAI AG, Switzerland). This software allows to detect several different biomarkers in each single OCT scan, including subretinal fluid (SRF), intraretinal fluid (IRF), hyperreflective foci (HF) and flat irregular pigment epithelium detachment (FIPED). The presence of SRF was considered as a necessary inclusion criterion for performing biomarker analysis and OCT slabs without SRF presence were excluded from the analysis.

Results: Overall, 160 eyes of 144 patients with CSC were enrolled, out of which 100 (62.5%) eyes were diagnosed with cCSC and 60 eyes (34.5%) with aCSC. In the OCT slabs showing presence of SRF the presence of biomarkers was found to be clinically relevant (> 50%) for HF and FIPED in aCSC and cCSC. HF had an average percentage of 81% (± 20) in the cCSC group and 81% (± 15) in the aCSC group (p = 0.4295) and FIPED had a mean percentage of 88% (± 18) in cCSC vs. 89% (± 15) in the aCSC (p = 0.3197).

Conclusion: We demonstrate that HF and FIPED are OCT biomarkers positively associated with CSC when present at baseline. While both HF and FIPED biomarkers could aid in CSC diagnosis, they could not distinguish between aCSC and cCSC at the first visit. AI-assisted biomarker detection shows promise for reducing invasive imaging needs, but further validation through longitudinal studies is needed.

Background: The use of povidone-iodine for ocular surface asepsis is widespread for intravitreal injections. They became frequent procedures, leading to serial exposure of patients' eyes to iodinated solutions. In this study, we investigate the changes in the ocular surface in patients submitted to repeated use of povidine for intravitreal injection of anti-VEGF asepsis, analyzing Ocular Surface Disease Index, non-invasive break up time, blinking quality, lipid layer, meniscus height and osmolarity.

Methods: This case-control study included 34 individuals (68 eyes), 14 males, 20 females aged 48 to 94. Inclusion criteria were individuals who received application of 2% povidone-iodine eyedrops for intravitreal injections treatment with the non-treated contralateral eye used as control. Ocular surface examinations were performed at a single occasion. A pre-intravitreal injection asepsis protocol with povidone-iodine was applied. All statistical analysis was performed using the STATA® 18.0 Software and a p-value = 0.05 was considered as the statistical significance value in all tests.

Results: The median number of IVIs in treated eyes was 12 (range 6-20). The results in treated eyes compared with untreated eyes were respectively : median OSDI 16 (IQR 6-39) and 12.5 (IQR 8-39) (p = 0.380); mean NIBUT 10.30 (SD ± 2.62) and 10.78 (SD ± 2.92) ( s, p = 0.476); median blinking quality 100 (IQR 100) and 100 (IQR 100 ) (%, p = 0.188); median lipid layer 87 (IQR 77-90) and 86 (IQR 74-100) (nm, p = 0.451); median meniscus height 0.22 (IQR 0.19-0,31) and 0.24 (IQR 0.20-0.27) (mm, p = 0.862), median Meibomian gland atrophy 33 (IQR 24-45) and 31.5 (IQR 25-39) (%, p = 0.524); and mean osmolarity 306.6 (SD ± 21.13) and 313.8 (SD ± 29) (mOsm, p = 0.297). There was no statistically significant relationship between the repetitive use of 2% iodinated solution and signs or symptoms compatible with dry eye syndrome in this group of patients.

Conclusions: The findings suggest that 2% povidone iodine is a safe and efficacious agent for ocular surface antisepsis during intravitreal injections, not leading to substantial ocular surface modifications. This conclusion supports the continued use of povidone iodine in routine ophthalmic procedures without increased risk of inducing dry eye syndrome.

Background: The aim of this study is to evaluate near and distance visual acuity (VA) and their correlation with the National Eye Institute Visual Function Questionnaire (NEI VFQ-25) outcomes in patients with diabetic macular edema (DME) and macular edema due to retinal vein occlusion (RVO) treated with aflibercept.

Methods: In this prospective study, we included 87 eyes of patients diagnosed with DME (n = 61) and RVO (n = 26), who received aflibercept treatment and were followed until the 8th injection. Near VA was examined on the 1st, 2nd, 3rd, 4th, 6th, and 8th injection, and patients completed the NEI VFQ-25 on the 1st, 4th, and 8th aflibercept injection.

Results: The mean near VA at baseline in all eyes was 0.89 ± 0.12 logMAR. With every administration, there was a statistically significant improvement; on the 4th (0.70 ± 0.19; p = 0.000) and the 8th application (0.60 ± 0.19; p = 0.000). At baseline, the mean NEI VFQ-25 total score was 71 ± 14%, and improved to 81 ± 13% (p = 0.000) on the 8th injection. The most significant score gain was recorded in the near VA subscale (+ 20 ± 14%, p = 0.000). There was no statistically significant difference between DME and RVO group in the questionnaire or near VA outcomes.

Conclusion: Aflibercept treatment resulted in a remarkable improvement of near vision by 4 lines of logMAR optotype after the 8th application. The near vision questionnaire subscale, initially scoring the lowest, exhibited the greatest gain during the treatment period. This underscores the importance of near vision and reading ability for patients with DME and RVO.

Background: Diabetic retinopathy (DR) is the leading cause of adult blindness in the working age population worldwide, which can be prevented by early detection. Regular eye examinations are recommended and crucial for detecting sight-threatening DR. Use of artificial intelligence (AI) to lessen the burden on the healthcare system is needed.

Purpose: To perform a pilot cost-analysis study for detecting DR in a cohort of minority women with DM in Oslo, Norway, that have the highest prevalence of diabetes mellitus (DM) in the country, using both manual (ophthalmologist) and autonomous (AI) grading. This is the first study in Norway, as far as we know, that uses AI in DR- grading of retinal images.

Methods: On Minority Women's Day, November 1, 2017, in Oslo, Norway, 33 patients (66 eyes) over 18 years of age diagnosed with DM (T1D and T2D) were screened. The Eidon - True Color Confocal Scanner (CenterVue, United States) was used for retinal imaging and graded for DR after screening had been completed, by an ophthalmologist and automatically, using EyeArt Automated DR Detection System, version 2.1.0 (EyeArt, EyeNuk, CA, USA). The gradings were based on the International Clinical Diabetic Retinopathy (ICDR) severity scale [1] detecting the presence or absence of referable DR. Cost-minimization analyses were performed for both grading methods.

Results: 33 women (64 eyes) were eligible for the analysis. A very good inter-rater agreement was found: 0.98 (P < 0.01), between the human and AI-based EyeArt grading system for detecting DR. The prevalence of DR was 18.6% (95% CI: 11.4-25.8%), and the sensitivity and specificity were 100% (95% CI: 100-100% and 95% CI: 100-100%), respectively. The cost difference for AI screening compared to human screening was $143 lower per patient (cost-saving) in favour of AI.

Conclusion: Our results indicate that The EyeArt AI system is both a reliable, cost-saving, and useful tool for DR grading in clinical practice.

Background: This study aims to investigate the factors affecting the vitreous levels of pigment epithelium-derived factor (PEDF) and vascular endothelial growth factor (VGEF) among patients with pars plana vitrectomy (PPV). Also, this study correlates the levels of PEDF with RRD characteristics.

Methods: All patients who were scheduled for PPV for any indication were included in the study. They were divided into a case group which included patients with advanced PDR and a control group which included the remaining diagnoses. During the PPV, an undiluted vitreous sample was taken and the enzyme-linked immunosorbent assay method was utilized to measure the levels of VEGF and PEDF.

Results: Eighty eyes were involved. Patients diagnosed with advanced PDR and endophthalmitis exhibited higher levels of VEGF. PEDF was affected inversely by the age of the patients and PEDF levels were higher in RRD and endophthalmitis cases. In patients with RRD, the level of PEDF was higher if the tear was found inferiorly, if the macula was detached, and with a longer duration of RRD.

Conclusions: This study highlights the clinical importance of those biomarkers. Anti-VEGF-based treatment is the mainstay against PDR. PEDF may show a promising predictive values regarding patients with RRD.

Background: Code-free deep learning (CFDL) is a novel tool in artificial intelligence (AI). This study directly compared the discriminative performance of CFDL models designed by ophthalmologists without coding experience against bespoke models designed by AI experts in detecting retinal pathologies from optical coherence tomography (OCT) videos and fovea-centered images.

Methods: Using the same internal dataset of 1,173 OCT macular videos and fovea-centered images, model development was performed simultaneously but independently by an ophthalmology resident (CFDL models) and a postdoctoral researcher with expertise in AI (bespoke models). We designed a multi-class model to categorize video and fovea-centered images into five labels: normal retina, macular hole, epiretinal membrane, wet age-related macular degeneration and diabetic macular edema. We qualitatively compared point estimates of the performance metrics of the CFDL and bespoke models.

Results: For videos, the CFDL model demonstrated excellent discriminative performance, even outperforming the bespoke models for some metrics: area under the precision-recall curve was 0.984 (vs. 0.901), precision and sensitivity were both 94.1% (vs. 94.2%) and accuracy was 94.1% (vs. 96.7%). The fovea-centered CFDL model overall performed better than video-based model and was as accurate as the best bespoke model.

Conclusion: This comparative study demonstrated that code-free models created by clinicians without coding expertise perform as accurately as expert-designed bespoke models at classifying various retinal pathologies from OCT videos and images. CFDL represents a step forward towards the democratization of AI in medicine, although its numerous limitations must be carefully addressed to ensure its effective application in healthcare.

Purpose: Our objective was to compare the serum Adropin levels between patients with wet-type Age-Related Macular Degeneration (AMD) and otherwise healthy individuals.

Method: The study included 45 patients with wet-type AMD and 45 individuals without age-related macular degeneration. Patients with co-morbidities such as diabetes, hypertension, autoimmune diseases, and a previous history of visual impairment; were excluded. FBS, Hemoglobin A1C (HbA1C), lipid profile, and serum Adropin level were checked.

Results: The mean serum Adropin level of patients with wet-type AMD was significantly lower than the control group (P-value < 0.001). Also, the mean High-sensitivity C-reactive protein ( hsCRP) level and High Density Lipoprotein (HDL) were significantly higher in wet-type AMD patients (P-value = 0.031 and < 0.001 respectively).

Conclusions: In our study, wet-type AMD was associated with a lower level of serum Adropin. Because of Adropin involvement in glucose metabolism and age-related changes, it may have a role in the pathogenesis of AMD, but it requires more investigations at the molecular level to elucidate its function.

Purpose: Although diabetes is highly prevalent in patients with MacTel, progression to severe non-proliferative (NPDR) and proliferative diabetic retinopathy (PDR) is rarely reported. We report multimodal imaging features of sight-threatening diabetic retinopathy (STDR) in eyes with macular telangiectasia type 2 (MacTel).

Methods: Retrospective case series of seven participants of the MacTel Study at the Moorfields Eye Hospital NHS Foundation Trust study site and one patient from the Institute of Retina and Vitreous of Londrina, Brazil. Sight threatening diabetic retinopathy was defined as severe NPDR, PDR or diabetic macular edema.

Results: We report imaging features of 16 eyes of eight patients (7/8, 87.5% female) with diagnoses of MacTel and type 2 diabetes mellitus with STDR. Mean (SD) age was 56 (8.3) years. Patients were followed-up for a mean time of 9.1 (4.7) years. A total of 10/16 (62.5%) eyes showed PDR and 2/16 (12.5%) eyes presented a macular epiretinal neovascularization.

Conclusions: People with diabetes mellitus and MacTel may not be protected from STDR as previously reported. Although the two diseases rarely co-exist, regular monitoring for diabetic retinopathy progression is recommended according to baseline retinopathy severity grades in line with established international guidelines. The presence of MacTel may not modify extended screening intervals, but there is no current evidence. The limited case series in the literature support treatment for complications and should follow the standard of care for either condition. Due to dual pathology, reactivation may be difficult to diagnose on standard imaging and multimodal imaging is recommended.