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.

Frequency and distribution of ophthalmic surgical procedures among patients with inherited retinal diseases.

OBJECTIVE OR PURPOSE: In this study, we aimed to characterize the frequency and distribution of ocular surgeries in patients with inherited retinal diseases (IRDs) and evaluate associated patient and disease factors. DESIGN: Retrospective cohort. PARTICIPANTS: Subjects 18 years and older who were followed at the Johns Hopkins Genetic Eye Disease (GEDi) Center. METHODS: We studied a retrospective cohort of patients with an IRD diagnosis to analyze the occurrence of laser and incisional surgeries. Subjects were categorized into two groups: central dysfunction (macular/cone/cone-rod dystrophy, "MCCRD group") and panretinal or peripheral dysfunction (retinitis pigmentosa-like, "RP group"). Genetic testing status was recorded. The association of patient and disease factors on the frequency, distribution, and timing of surgeries was analyzed. MAIN OUTCOME MEASURE: Prevalence, prevalence odds ratio (POR), hazard ratio (HR) of ophthalmic procedures by phenotype. RESULTS: A total of 1472 eyes of 736 subjects were evaluated. Among them, 31.3% (n = 230) had undergone ocular surgery, and 78.3% of those (n=180/230) had a history of more than one surgery. A total of 602 surgical procedures were analyzed. Cataract extraction with intraocular lens implantation (CEIOL) was the most common (51.2%), followed by YAG capsulotomy, refractive surgery, retinal surgery, and others. CEIOL occurred more frequently in RP than in MCCRD subjects (POR 2.59, p = 0.002). RP subjects underwent CEIOL at a younger age than MCCRD patients (HR = 2.11, p < 0.001). CONCLUSION: Approximately one-third of IRD patients had a history of laser or incisional surgery. CEIOL was the most common surgery; its frequency and timing may be associated with IRD phenotype. This data may inform the design of prospective research. Such efforts may illuminate routine clinical decision-making and contribute to surgical strategy development for cell and gene therapy delivery.

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.

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.

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.

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.

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.

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.

Swept Source Optical Coherence Tomography Angiography Findings in Birdshot Chorioretinitis: A Cross Sectional Study of 21 Patients.

PURPOSE: Birdshot chorioretinitis (BSCR) is a form of posterior uveitis that is classically characterized by hypopigmented choroidal lesions outside of the major arcades. However, little is known about the extent of choroidal involvement in the macula. We aim to describe the vascular abnormalities observed at the level of the choriocapillaris (CC) in the maculae of BSCR patients, using swept source optical coherence tomography angiography (SS-OCTA). METHODS: A cross-sectional, observational study was conducted. Eligible patients underwent clinical examination and SS-OCTA imaging. The main outcome measures were the frequency of vascular abnormalities observed at the level of the CC on SS-OCTA and foveal choriocapillaris vascular density (CVD). RESULTS: Twenty-one patients were included, with a median age of 61.5 years. All patients had bilateral disease with a median disease duration of 6 years. All but one patient received systemic immunosuppressive drug therapy, and 19 patients had suppressed inflammation on treatment at the time of the SS-OCTA assessment. Of the 42 affected eyes, 39 (92.9%) had gradable SS-OCTA images, with a mean LogMAR visual acuity of 0.18 (Snellen equivalent 20/30). In total, 34 of 39 (87.2%) eyes had some degree of pathologic flow loss, and after controlling for patient age and disease activity, both worse VA and longer disease duration remained statistically significantly associated with reduced foveal CVD. CONCLUSIONS: Our findings suggest that pathologic CC flow loss in the macula is frequently encountered and may contribute to visual function decline in patients with BSCR. Further studies with longitudinal follow-up are needed to characterize the evolution of these areas of pathologic CC flow loss over time.

Direct Gene Expression Profile Prediction for Uveal Melanoma from Digital Cytopathology Images via Deep Learning and Salient Image Region Identification.

Objective: To demonstrate that deep learning (DL) methods can produce robust prediction of gene expression profile (GEP) in uveal melanoma (UM) based on digital cytopathology images. Design: Evaluation of a diagnostic test or technology. Subjects Participants and Controls: Deidentified smeared cytology slides stained with hematoxylin and eosin obtained from a fine needle aspirated from UM. Methods: Digital whole-slide images were generated by fine-needle aspiration biopsies of UM tumors that underwent GEP testing. A multistage DL system was developed with automatic region-of-interest (ROI) extraction from digital cytopathology images, an attention-based neural network, ROI feature aggregation, and slide-level data augmentation. Main Outcome Measures: The ability of our DL system in predicting GEP on a slide (patient) level. Data were partitioned at the patient level (73% training; 27% testing). Results: In total, our study included 89 whole-slide images from 82 patients and 121 388 unique ROIs. The testing set included 24 slides from 24 patients (12 class 1 tumors; 12 class 2 tumors; 1 slide per patient). Our DL system for GEP prediction achieved an area under the receiver operating characteristic curve of 0.944, an accuracy of 91.7%, a sensitivity of 91.7%, and a specificity of 91.7% on a slide-level analysis. The incorporation of slide-level feature aggregation and data augmentation produced a more predictive DL model (P = 0.0031). Conclusions: Our current work established a complete pipeline for GEP prediction in UM tumors: from automatic ROI extraction from digital cytopathology whole-slide images to slide-level predictions. Our DL system demonstrated robust performance and, if validated prospectively, could serve as an image-based alternative to GEP testing.

Social Determinants of Health and Impact on Screening, Prevalence, and Management of Diabetic Retinopathy in Adults: A Narrative Review.

Diabetic retinal disease (DRD) is the leading cause of blindness among working-aged individuals with diabetes. In the United States, underserved and minority populations are disproportionately affected by diabetic retinopathy and other diabetes-related health outcomes. In this narrative review, we describe racial disparities in the prevalence and screening of diabetic retinopathy, as well as the wide-range of disparities associated with social determinants of health (SDOH), which include socioeconomic status, geography, health-care access, and education.

Global Current Practice Patterns for the Management of Hyphema.

Purpose: Hyphema is a sequela of ocular trauma and can be associated with significant morbidity. Management of this condition is variable and can depend on individual institutional guidelines. We aimed to summarize current practices in hyphema management across ophthalmological institutions worldwide. Methods: A cross-sectional online survey was conducted across North America, Asia, South America, Africa, Europe, and Australia from August 2020 to January 2021. The survey assessed the existing practices in the management of hyphema at each institution. Results: For layered hyphema, topical steroids were routinely administered by 34 (of 36 respondents, 94.4%) institutions, of which prednisolone was the preferred choice (n = 32, 88.9%). Topical cycloplegics were used at 34 (94.4%) institutions. No institution reported routine use of antifibrinolytics. Head elevation was the most deployed procedure to promote hyphema reabsorption (n = 31, 86.3%), followed by partial bed rest (n = 21, 58.3%). The majority of institutions (n = 25, 69.4%) did not routinely pursue admission for hyphema patients, although 75.0% of institutions (n = 27) scheduled follow-up visits within 48 hours of presentation. Additionally, few institutions performed routine sickle cell trait testing for patients presenting with hyphema (n = 6, 16.7%). The decision to perform anterior chamber washout varied and was often based on intraocular pressure and the speed of hyphema resolution. Conclusion: Unanimity of international institutions on hyphema management is lacking. As it stands, many current interventions have unconvincing evidence supporting their use. Evidence-based guidelines would be beneficial in guiding decision-making on hyphema management. Additionally, areas of consensus can be used as foundations for future standard of care investigations.