Autonomous artificial intelligence for diabetic eye disease increases access and health equity in underserved populations.

Diabetic eye disease (DED) is a leading cause of blindness in the world. Annual DED testing is recommended for adults with diabetes, but adherence to this guideline has historically been low. In 2020, Johns Hopkins Medicine (JHM) began deploying autonomous AI for DED testing. In this study, we aimed to determine whether autonomous AI implementation was associated with increased adherence to annual DED testing, and how this differed across patient populations. JHM primary care sites were categorized as "non-AI" (no autonomous AI deployment) or "AI-switched" (autonomous AI deployment by 2021). We conducted a propensity score weighting analysis to compare change in adherence rates from 2019 to 2021 between non-AI and AI-switched sites. Our study included all adult patients with diabetes (>17,000) managed within JHM and has three major findings. First, AI-switched sites experienced a 7.6 percentage point greater increase in DED testing than non-AI sites from 2019 to 2021 (p < 0.001). Second, the adherence rate for Black/African Americans increased by 12.2 percentage points within AI-switched sites but decreased by 0.6% points within non-AI sites (p < 0.001), suggesting that autonomous AI deployment improved access to retinal evaluation for historically disadvantaged populations. Third, autonomous AI is associated with improved health equity, e.g. the adherence rate gap between Asian Americans and Black/African Americans shrank from 15.6% in 2019 to 3.5% in 2021. In summary, our results from real-world deployment in a large integrated healthcare system suggest that autonomous AI is associated with improvement in overall DED testing adherence, patient access, and health equity.

[Preliminary study on the role and mechanism of IL-6 receptor antagonists in improving post-infarction ventricular arrhythmia].

Objective: To investigate the effect of tocilizumab (TCZ) on ventricular arrhythmias (VAs) after myocardial infarction (MI) in Sprague-Dawley rats and explore its potential mechanism. Methods: The random number table method was used to divide 32 adult male Sprague-Dawley rats into 4 groups: Sham group, TCZ group, MI group and MI+TCZ group, with 8 rats in each group. The MI model was established by ligation of the left anterior descending branch of the coronary artery in the MI and MI+TCZ groups, and only sutured without ligation in the Sham and TCZ groups. TCZ was injected into the left superior cervical ganglion (SCG) of rats in the TCZ and MI+TCZ groups after successful modeling or sham operation, and the same amount of normal saline was injected in the Sham and MI groups. 24 h after successful modeling, ECG of rats in each group was recorded, heart rate variability (HRV, including low frequency power (LF), high frequency power (HF), LF/HF ratio), QT interval, QTc interval were calculated, and left ventricular effective refractory period (ERP) and VA inducibility were measured. Myocardial infarct size and tissue changes were observed with triphenyl tetrazolium chloride staining and HE staining. Real-time PCR analysis was used to detect the messager RNA (mRNA) expression of interleukin-6 (IL-6) and signal transducer and activator of transcription (STAT) 3 in SCG and potassium voltage-gated channel subfamily D member 2 (Kcnd2) in myocardial infarction periphery. The expression of c-fos in SCG was detected by immunofluorescence staining. Results: Compared with Sham group and MI+TCZ group, rats in MI group had higher LF and LF/HF ratio, longer QT interval and QTc interval, more VAs induced, lower HF and shorter ERP (P all<0.05). Triphenyl tetrazolium chloride staining and HE staining showed that rats in the Sham and TCZ groups had normal myocardial tissue structure, those in the MI group had severe myocardial injury, and those in the MI+TCZ group had less myocardial injury than those in the MI group. Real-ime PCR analysis showed that compared with Sham group and MI+TCZ group, mRNA expression levels of IL-6 and STAT3 in SCG of rats in MI group were higher, and mRNA expression level of myocardial Kcnd2 was lower (P all<0.05). Immunofluorescence staining showed that the content of c-fos in SCG of rats in MI group was higher than that of Sham group and MI+TCZ group (P all<0.05). Conclusions: TCZ may reduce neural activity of the SCG after MI by inhibiting the IL-6/STAT3 signaling pathway, thereby alleviating myocardial injury and inhibiting VAs.

Dipeptidyl peptidase 4 inhibitors, sodium glucose cotransporter 2 inhibitors, and glucagon-like peptide 1 receptor agonists do not worsen diabetic macular edema.

AIMS: There are limited studies on dipeptidyl-peptidase 4 inhibitor (DPP-4i), sodium glucose cotransporter 2 inhibitor (SGLT2-i), and glucagon-like peptide 1 (GLP-1) receptor agonist use and occurrence of diabetic macular edema (DME). The objective of this study was to determine the association between DPP-4i, SGLT2-i, and GLP-1 receptor agonist use and occurrence of DME. METHODS: Proportional hazard models were used to evaluate the change in hazard of developing DME associated with DPP-4i, SGLT2-i, or GLP-1 receptor agonist use. Models accounted for age at DR diagnosis, DR severity (proliferative vs non-proliferative stage), time-weighted average of HbA1c level, sex, and self-reported race/ethnicity. A p-value ≤ 0.05 was considered statistically significant. RESULTS: The hazard ratio of developing DME after diagnosis of DR was 1.2 (CI = 0.75 to 1.99; p = 0.43) for DPP-4i use, 0.93 (CI = 0.54 to 1.61; p = 0.81) for GLP-1 receptor agonist use, 0.82 (CI = 0.20 to 3.34; p = 0.78) for SGLT2-i use, 1.1 (CI = 0.75 to 1.59; p = 0.66) for any one medication use, 1.1 (CI = 0.62 to 2.09; p = 0.68) and for any two or more medications use. CONCLUSIONS: We did not find an association between DPP-4i, SGLT2-i, or GLP-1 receptor agonist use and increased hazard of development of DME among patients with DR.

Impact of polygenic risk score for triglyceride trajectory and diabetic complications in subjects with type 2 diabetes based on large electronic medical record data from Taiwan: a case control study.

BACKGROUND: The prevalence of diabetic dyslipidemia has gradually increased worldwide and individuals with hypertriglyceridemia often have a high polygenic burden of triglyceride (TG)-increasing variants. However, the contribution of genetic variants to dyslipidemia in patients with type 2 diabetes (T2D) remains limited. Therefore, in this study, we aimed to investigate the genetic characteristics of longitudinal changes in TG levels among patients with T2D and summarize the genetic effects of polygenic risk score (PRS) on TG trajectory and risk of diabetic complications. METHODS: We conducted a case-control study. A total of 11,312 patients with T2D with longitudinal TG and genetic data were identified from a large hospital database in Taiwan. We then performed a genome-wide association study and calculated the relative PRS. RESULTS: In total, 21 single-nucleotide polymorphisms (SNPs) related to TG trajectory were identified and yielded an area under the receiver operating characteristic curve (ROC) of 0.712 for high TG trajectory risk among Taiwanese patients with T2D. A cumulative genetic effect was observed for high TG trajectory, even when considering the adherence of a lipid-lowering agent in stratified analysis. An increased PRS increases high TG trajectory risk in a logistic regression model (odds ratio = 1.55; 95% confidence interval [CI] = 1.31-1.83 in the validation cohort). The TG-specific PRS was associated with the risk of diabetic microvascular complications, including diabetic retinopathy and nephropathy (with hazard ratios of 1.11 [95% CI = 1.01-1.21, P = 0.027] and 1.05 [95% CI = 1.01-1.1, P = 0.018], respectively). CONCLUSIONS: This study may contribute to the identification of patients with T2D who are at risk of abnormal TG levels and diabetic microvascular complications using polygenic information.

Autonomous Artificial Intelligence Increases Access and Health Equity in Underserved Populations with Diabetes.

Diabetic eye disease (DED) is a leading cause of blindness in the world. Early detection and treatment of DED have been shown to be both sight-saving and cost-effective. As such, annual testing for DED is recommended for adults with diabetes and is a Healthcare Effectiveness Data and Information Set (HEDIS) measure. However, adherence to this guideline has historically been low, and access to this sight-saving intervention has particularly been limited for specific populations, such as Black or African American patients. In 2018, the US Food and Drug Agency (FDA) De Novo cleared autonomous artificial intelligence (AI) for diagnosing DED in a primary care setting. In 2020, Johns Hopkins Medicine (JHM), an integrated healthcare system with over 30 primary care sites, began deploying autonomous AI for DED testing in some of its primary care clinics. In this retrospective study, we aimed to determine whether autonomous AI implementation was associated with increased adherence to annual DED testing, and whether this was different for specific populations. JHM primary care sites were categorized as "non-AI" sites (sites with no autonomous AI deployment over the study period and where patients are referred to eyecare for DED testing) or "AI-switched" sites (sites that did not have autonomous AI testing in 2019 but did by 2021). We conducted a difference-in-difference analysis using a logistic regression model to compare change in adherence rates from 2019 to 2021 between non-AI and AI-switched sites. Our study included all adult patients with diabetes managed within our health system (17,674 patients for the 2019 cohort and 17,590 patients for the 2021 cohort) and has three major findings. First, after controlling for a wide range of potential confounders, our regression analysis demonstrated that the odds ratio of adherence at AI-switched sites was 36% higher than that of non-AI sites, suggesting that there was a higher increase in DED testing between 2019 and 2021 at AI-switched sites than at non-AI sites. Second, our data suggested autonomous AI improved access for historically disadvantaged populations. The adherence rate for Black/African Americans increased by 11.9% within AI-switched sites whereas it decreased by 1.2% within non-AI sites over the same time frame. Third, the data suggest that autonomous AI improved health equity by closing care gaps. For example, in 2019, a large adherence rate gap existed between Asian Americans and Black/African Americans (61.1% vs. 45.5%). This 15.6% gap shrank to 3.5% by 2021. In summary, our real-world deployment results in a large integrated healthcare system suggest that autonomous AI improves adherence to a HEDIS measure, patient access, and health equity for patients with diabetes - particularly in historically disadvantaged patient groups. While our findings are encouraging, they will need to be replicated and validated in a prospective manner across more diverse settings.

Use of Diabetes Technologies and Retinopathy in Adults With Type 1 Diabetes.

Importance: Diabetic retinopathy (DR) is a complication of diabetes that can lead to vision loss. Outcomes of continuous glucose monitoring (CGM) and insulin pump use in DR are not well understood. Objective: To assess the use of CGM, insulin pump, or both, and DR and proliferative diabetic retinopathy (PDR) in adults with type 1 diabetes (T1D). Design, Setting, and Participants: A retrospective cohort study of adults with T1D in a tertiary diabetes center and ophthalmology center was conducted from 2013 to 2021, with data analysis performed from June 2022 to April 2023. Exposure: Use of diabetes technologies, including insulin pump, CGM, and both CGM and insulin pump. Main Outcomes and Measures: The primary outcome was development of DR or PDR. A secondary outcome was the progression of DR for patients in the longitudinal cohort. Multivariable logistic regression models assessed for development of DR and PDR and association with CGM and insulin pump use. Results: A total of 550 adults with T1D were included (median age, 40 [IQR, 28-54] years; 54.4% female; 24.5% Black or African American; and 68.4% White), with a median duration of diabetes of 20 (IQR, 10-30) years, and median hemoglobin A1c (HbA1c) of 7.8% (IQR, 7.0%-8.9%). Overall, 62.7% patients used CGM, 58.2% used an insulin pump, and 47.5% used both; 44% (244 of 550) of the participants had DR at any point during the study. On univariate analysis, CGM use was associated with lower odds of DR and PDR, and CGM with pump was associated with lower odds of PDR (all P < .05), compared with no CGM use. Multivariable logistic regression adjusting for age, sex, race and ethnicity, diabetes duration, microvascular and macrovascular complications, insurance type, and mean HbA1c, showed that CGM was associated with lower odds of DR (odds ratio [OR], 0.52; 95% CI, 0.32-0.84; P = .008) and PDR (OR, 0.42; 95% CI, 0.23-0.75; P = .004), compared with no CGM use. In the longitudinal analysis of participants without baseline PDR, 79 of 363 patients (21.8%) had progression of DR during the study. Conclusions and Relevance: In this cohort study of adults with T1D, CGM use was associated with lower odds of developing DR and PDR, even after adjusting for HbA1c. These findings suggest that CGM may be useful for diabetes management to mitigate risk for DR and PDR.

Ten-Year Outcome of Boston Type I Keratoprosthesis Surgery at a Tertiary Care Center.

PURPOSE: The aim of this study was to report long-term outcomes of patients who have undergone Boston type I keratoprosthesis (KPro) surgery. METHODS: This study was a retrospective review. Inclusion criteria were KPro surgery between 2006 and 2012 and at least 10 years of follow-up. Demographics, ocular history, surgery indication, clinical variables, and postsurgical outcomes were recorded. Descriptive statistical analysis was performed. RESULTS: We identified 75 patients with KPro implantation, and 17 patients with at least 10 years of follow-up (median = 11.1 years; range, 10.0-12.8 years) were included. Of 17 eyes, 11 (64.8%) had their original device in situ, 3 (17.6%) had their second device in situ, 1 (5.9%) had the device removed and replaced with a donor keratoplasty, and 2 (11.8%) were enucleated. At the last follow-up, 11 eyes (64.7%) were able to maintain improvement in vision, 5 (29.4%) had worsened vision, 1 (5.9%) had stable vision, and 9 (52.9%) had visual acuity

Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial.

Diabetic retinopathy can be prevented with screening and early detection. We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates in a racially and ethnically diverse youth population. AI for Children's diabetiC Eye ExamS (NCT05131451) is a parallel randomized controlled trial that randomized youth (ages 8-21 years) with type 1 and type 2 diabetes to intervention (autonomous artificial intelligence diabetic eye exam at the point of care), or control (scripted eye care provider referral and education) in an academic pediatric diabetes center. The primary outcome was diabetic eye exam completion rate within 6 months. The secondary outcome was the proportion of participants who completed follow-through with an eye care provider if deemed appropriate. Diabetic eye exam completion rate was significantly higher (100%, 95%CI: 95.5%, 100%) in the intervention group (n = 81) than the control group (n = 83) (22%, 95%CI: 14.2%, 32.4%)(p < 0.001). In the intervention arm, 25/81 participants had an abnormal result, of whom 64% (16/25) completed follow-through with an eye care provider, compared to 22% in the control arm (p < 0.001). Autonomous AI increases diabetic eye exam completion rates in youth with diabetes.

Retinal characteristics in eyes with pathologic myopia among individuals self-identifying as Black.

OBJECTIVE: Investigate retinal characteristics of pathologic myopia (PM) among patients self-identifying as Black. DESIGN: Retrospective cohort single-institution retrospective medical record review. METHODS: Adult patients between January 2005 and December 2014 with International Classification of Diseases (ICD) codes consistent with PM and given 5-year follow-up were evaluated. The Study Group consisted of patients self-identifying as Black, and the Comparison Group consisted of those not self-identifying as Black. Ocular features at study baseline and 5-year follow-up visit were evaluated. RESULTS: Among 428 patients with PM, 60 (14%) self-identified as Black and 18 (30%) had baseline and 5-year follow-up visits. Of the remaining 368 patients, 63 were in the Comparison Group. For the study (n = 18) and Comparison Group (n = 29), median (25th percentile, 75th percentile) baseline visual acuity was 20/40 (20/25, 20/50) and 20/32 (20/25, 20/50) in the better-seeing eye and 20/70 (20/50, 20/1400) and 20/100 (20/50, 20/200), respectively, in the worse-seeing eye. In the eyes that did not have choroidal neovascularization (CNV) in the study and Comparison Group, median study baseline optical coherence tomography central subfield thickness was 196 µm (169, 306 µm) and 225 µm (191, 280 µm), respectively, in the better-seeing eye and 208 µm (181, 260 µm) and 194 µm (171, 248 µm), respectively, in the worse-seeing eye. Baseline prevalence of CNV was 1 Study Group eye (3%) and 20 Comparison Group eyes (34%). By the 5-year visit, zero (0%) and 4 (15%) additional eyes had CNV in the study and Comparison Group, respectively. CONCLUSION: These findings suggest that the prevalence and incidence of CNV may be lower in patients with PM self-identifying as Black when compared with individuals of other races.

Risk Factors for Nondiagnostic Imaging in a Real-World Deployment of Artificial Intelligence Diabetic Retinal Examinations in an Integrated Healthcare System: Maximizing Workflow Efficiency Through Predictive Dilation.

OBJECTIVE: In the pivotal clinical trial that led to Food and Drug Administration De Novo "approval" of the first fully autonomous artificial intelligence (AI) diabetic retinal disease diagnostic system, a reflexive dilation protocol was used. Using real-world deployment data before implementation of reflexive dilation, we identified factors associated with nondiagnostic results. These factors allow a novel predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori to maximize efficiency and patient satisfaction. METHODS: Retrospective review of patients who were assessed with autonomous AI at Johns Hopkins Medicine (8/2020 to 5/2021). We constructed a multivariable logistic regression model for nondiagnostic results to compare characteristics of patients with and without diagnostic results, using adjusted odds ratio (aOR). P < .05 was considered statistically significant. RESULTS: Of 241 patients (59% female; median age = 59), 123 (51%) had nondiagnostic results. In multivariable analysis, type 1 diabetes (T1D, aOR = 5.82, 95% confidence interval [CI]: 1.45-23.40, P = .01), smoking (aOR = 2.86, 95% CI: 1.36-5.99, P = .005), and age (every 10-year increase, aOR = 2.12, 95% CI: 1.62-2.77, P < .001) were associated with nondiagnostic results. Following feature elimination, a predictive model was created using T1D, smoking, age, race, sex, and hypertension as inputs. The model showed an area under the receiver-operator characteristics curve of 0.76 in five-fold cross-validation. CONCLUSIONS: We used factors associated with nondiagnostic results to design a novel, predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori. This new workflow has the potential to be more efficient than reflexive dilation, thus maximizing the number of at-risk patients receiving their diabetic retinal examinations.

Vision transformers: The next frontier for deep learning-based ophthalmic image analysis.

Deep learning is the state-of-the-art machine learning technique for ophthalmic image analysis, and convolutional neural networks (CNNs) are the most commonly utilized approach. Recently, vision transformers (ViTs) have emerged as a promising approach, one that is even more powerful than CNNs. In this focused review, we summarized studies that applied ViT-based models to analyze color fundus photographs and optical coherence tomography images. Overall, ViT-based models showed robust performances in the grading of diabetic retinopathy and glaucoma detection. While some studies demonstrated that ViTs were superior to CNNs in certain contexts of use, it is unclear how widespread ViTs will be adopted for ophthalmic image analysis, since ViTs typically require even more training data as compared to CNNs. The studies included were identified from the PubMed and Google Scholar databases using keywords relevant to this review. Only original investigations through March 2023 were included.

Comparative analysis of alignment algorithms for macular optical coherence tomography imaging.

BACKGROUND: Optical coherence tomography (OCT) is the most important and commonly utilized imaging modality in ophthalmology and is especially crucial for the diagnosis and management of macular diseases. Each OCT volume is typically only available as a series of cross-sectional images (B-scans) that are accessible through proprietary software programs which accompany the OCT machines. To maximize the potential of OCT imaging for machine learning purposes, each OCT image should be analyzed en bloc as a 3D volume, which requires aligning all the cross-sectional images within a particular volume. METHODS: A dataset of OCT B-scans obtained from 48 age-related macular degeneration (AMD) patients and 50 normal controls was used to evaluate five registration algorithms. After alignment of B-scans from each patient, an en face surface map was created to measure the registration quality, based on an automatically generated Laplace difference of the surface map-the smoother the surface map, the smaller the average Laplace difference. To demonstrate the usefulness of B-scan alignment, we trained a 3D convolutional neural network (CNN) to detect age-related macular degeneration (AMD) on OCT images and compared the performance of the model with and without B-scan alignment. RESULTS: The mean Laplace difference of the surface map before registration was 27 ± 4.2 pixels for the AMD group and 26.6 ± 4 pixels for the control group. After alignment, the smoothness of the surface map was improved, with a mean Laplace difference of 5.5 ± 2.7 pixels for Advanced Normalization Tools Symmetric image Normalization (ANTs-SyN) registration algorithm in the AMD group and a mean Laplace difference of 4.3 ± 1.4.2 pixels for ANTs in the control group. Our 3D CNN achieved superior performance in detecting AMD, when aligned OCT B-scans were used (AUC 0.95 aligned vs. 0.89 unaligned). CONCLUSIONS: We introduced a novel metric to quantify OCT B-scan alignment and compared the effectiveness of five alignment algorithms. We confirmed that alignment could be improved in a statistically significant manner with readily available alignment algorithms that are available to the public, and the ANTs algorithm provided the most robust performance overall. We further demonstrated that alignment of OCT B-scans will likely be useful for training 3D CNN models.

Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review.

Age-related macular degeneration (AMD) is one of the leading causes of blindness in the elderly, more commonly in developed countries. Optical coherence tomography (OCT) is a non-invasive imaging device widely used for the diagnosis and management of AMD. Deep learning (DL) uses multilayered artificial neural networks (NN) for feature extraction, and is the cutting-edge technique for medical image analysis for diagnostic and prognostication purposes. Application of DL models to OCT image analysis has garnered significant interest in recent years. In this review, we aimed to summarize studies focusing on DL models used in classification and detection of AMD. Additionally, we provide a brief introduction to other DL applications in AMD, such as segmentation, prediction/prognostication, and models trained on multimodal imaging.

Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema.

Importance: Best-corrected visual acuity (BCVA) is a measure used to manage diabetic macular edema (DME), sometimes suggesting development of DME or consideration of initiating, repeating, withholding, or resuming treatment with anti-vascular endothelial growth factor. Using artificial intelligence (AI) to estimate BCVA from fundus images could help clinicians manage DME by reducing the personnel needed for refraction, the time presently required for assessing BCVA, or even the number of office visits if imaged remotely. Objective: To evaluate the potential application of AI techniques for estimating BCVA from fundus photographs with and without ancillary information. Design, Setting, and Participants: Deidentified color fundus images taken after dilation were used post hoc to train AI systems to perform regression from image to BCVA and to evaluate resultant estimation errors. Participants were patients enrolled in the VISTA randomized clinical trial through 148 weeks wherein the study eye was treated with aflibercept or laser. The data from study participants included macular images, clinical information, and BCVA scores by trained examiners following protocol refraction and VA measurement on Early Treatment Diabetic Retinopathy Study (ETDRS) charts. Main Outcomes: Primary outcome was regression evaluated by mean absolute error (MAE); the secondary outcome included percentage of predictions within 10 letters, computed over the entire cohort as well as over subsets categorized by baseline BCVA, determined from baseline through the 148-week visit. Results: Analysis included 7185 macular color fundus images of the study and fellow eyes from 459 participants. Overall, the mean (SD) age was 62.2 (9.8) years, and 250 (54.5%) were male. The baseline BCVA score for the study eyes ranged from 73 to 24 letters (approximate Snellen equivalent 20/40 to 20/320). Using ResNet50 architecture, the MAE for the testing set (n = 641 images) was 9.66 (95% CI, 9.05-10.28); 33% of the values (95% CI, 30%-37%) were within 0 to 5 letters and 28% (95% CI, 25%-32%) within 6 to 10 letters. For BCVA of 100 letters or less but more than 80 letters (20/10 to 20/25, n = 161) and 80 letters or less but more than 55 letters (20/32 to 20/80, n = 309), the MAE was 8.84 letters (95% CI, 7.88-9.81) and 7.91 letters (95% CI, 7.28-8.53), respectively. Conclusions and Relevance: This investigation suggests AI can estimate BCVA directly from fundus photographs in patients with DME, without refraction or subjective visual acuity measurements, often within 1 to 2 lines on an ETDRS chart, supporting this AI concept if additional improvements in estimates can be achieved.

Artificial intelligence for ocular oncology.

PURPOSE OF REVIEW: The aim of this article is to provide an update on the latest applications of deep learning (DL) and classical machine learning (ML) techniques to the detection and prognostication of intraocular and ocular surface malignancies. RECENT FINDINGS: Most recent studies focused on using DL and classical ML techniques for prognostication purposes in patients with uveal melanoma (UM). SUMMARY: DL has emerged as the leading ML technique for prognostication in ocular oncological conditions, particularly in UM. However, the application of DL may be limited by the relatively rarity of these conditions.

Correlation between hyperreflective foci and visual function testing in eyes with intermediate age-related macular degeneration.

BACKGROUND: To investigate the relationship between intraretinal hyperreflective foci (HRF) and visual function in intermediate age-related macular degeneration (iAMD). METHODS: Retrospective, cross-sectional study. iAMD patients underwent spectral domain optical coherence tomography (SD-OCT) imaging and vision function testing: normal luminance best corrected visual acuity (VA), low luminance VA (LLVA), quantitative contrast sensitivity function (qCSF), low luminance qCSF (LLqCSF), and mesopic microperimetry. Each OCT volume was graded for the presence and number of HRF. Each HRF was graded for: separation from the retinal pigment epithelium (RPE), above drusen, and shadowing. Central drusen volume was calculated by the built-in functionality of the commercial OCT software after manual segmentation of the RPE and Bruch's membrane. RESULTS: HRF group: 11 eyes; 9 patients; mean age 75.7 years. No-HRF group: 11 eyes; 10 patients; mean age 74.8 years. In linear mixed effect model adjusting for cube-root transformed drusen volume, HRF group showed statistically significant worse VA, LLVA, LLqCSF, and microperimetry. HRF group showed worse cone function, as measured by our pre-defined multicomponent endpoint, incorporating LLVA, LLqCSF and microperimetry (p = 0.018). For eyes with HRF, # of HRF did not correlate with any functional measures; however, % of HRF separated from RPE and # of HRF that created shadowing were statistically associated with low luminance deficit (LLD). CONCLUSIONS: The association between the presence of HRF and worse cone visual function supports the hypothesis that eyes with HRF have more advanced disease.