Ophthalmology at the Forefront of Big Data Integration in Medicine: Insights from the IRIS Registry Database.

Ophthalmology stands at the vanguard of incorporating big data into medicine, as exemplified by the integration of The Intelligent Research in Sight (IRIS) Registry. This synergy cultivates patient-centered care, demonstrates real world efficacy and safety data for new therapies, and facilitates comprehensive population health insights. By evaluating the creation and utilization of the world's largest specialty clinical data registry, we underscore the transformative capacity of data-driven medical paradigms, current shortcomings, and future directions. We aim to provide a scaffold for other specialties to adopt big data integration into medicine.

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.

IMPORTANCE: The development of artificial intelligence (AI) and other machine diagnostic systems, also known as software as a medical device, and its recent introduction into clinical practice requires a deeply rooted foundation in bioethics for consideration by regulatory agencies and other stakeholders around the globe. OBJECTIVES: To initiate a dialogue on the issues to consider when developing a bioethically sound foundation for AI in medicine, based on images of eye structures, for discussion with all stakeholders. EVIDENCE REVIEW: The scope of the issues and summaries of the discussions under consideration by the Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group, as first presented during the Collaborative Community on Ophthalmic Imaging inaugural meeting on September 7, 2020, and afterward in the working group. FINDINGS: Artificial intelligence has the potential to improve health care access and patient outcome fundamentally while decreasing disparities, lowering cost, and enhancing the care team. Nevertheless, substantial concerns exist. Bioethicists, AI algorithm experts, as well as the Food and Drug Administration and other regulatory agencies, industry, patient advocacy groups, clinicians and their professional societies, other provider groups, and payors (i.e., stakeholders) working together in collaborative communities to resolve the fundamental ethical issues of nonmaleficence, autonomy, and equity are essential to attain this potential. Resolution impacts all levels of the design, validation, and implementation of AI in medicine. Design, validation, and implementation of AI warrant meticulous attention. CONCLUSIONS AND RELEVANCE: The development of a bioethically sound foundation may be possible if it is based in the fundamental ethical principles of nonmaleficence, autonomy, and equity for considerations for the design, validation, and implementation for AI systems. Achieving such a foundation will be helpful for continuing successful introduction into medicine before consideration by regulatory agencies. Important improvements in accessibility and quality of health care, decrease in health disparities, and lower cost thereby can be achieved. These considerations should be discussed with all stakeholders and expanded on as a useful initiation of this dialogue.

Artificial intelligence and ophthalmic surgery.

PURPOSE OF REVIEW: Artificial intelligence and deep learning have become important tools in extracting data from ophthalmic surgery to evaluate, teach, and aid the surgeon in all phases of surgical management. The purpose of this review is to highlight the ever-increasing intersection of computer vision, machine learning, and ophthalmic microsurgery. RECENT FINDINGS: Deep learning algorithms are being applied to help evaluate and teach surgical trainees. Artificial intelligence tools are improving real-time surgical instrument tracking, phase segmentation, as well as enhancing the safety of robotic-assisted vitreoretinal surgery. SUMMARY: Similar to strides appreciated in ophthalmic medical disease, artificial intelligence will continue to become an important part of surgical management of ocular conditions. Machine learning applications will help push the boundaries of what surgeons can accomplish to improve patient outcomes.

Automated and Computer-Assisted Detection, Classification, and Diagnosis of Diabetic Retinopathy.

Background: The introduction of artificial intelligence (AI) in medicine has raised significant ethical, economic, and scientific controversies. Introduction: Because an explicit goal of AI is to perform processes previously reserved for human clinicians and other health care personnel, there is justified concern about the impact on patient safety, efficacy, equity, and liability. Discussion: Systems for computer-assisted and fully automated detection, triage, and diagnosis of diabetic retinopathy (DR) from retinal images show great variation in design, level of autonomy, and intended use. Moreover, the degree to which these systems have been evaluated and validated is heterogeneous. We use the term DR AI system as a general term for any system that interprets retinal images with at least some degree of autonomy from a human grader. We put forth these standardized descriptors to form a means to categorize systems for computer-assisted and fully automated detection, triage, and diagnosis of DR. The components of the categorization system include level of device autonomy, intended use, level of evidence for diagnostic accuracy, and system design. Conclusion: There is currently minimal empirical basis to assert that certain combinations of autonomy, accuracy, or intended use are better or more appropriate than any other. Therefore, at the current stage of development of this document, we have been descriptive rather than prescriptive, and we treat the different categorizations as independent and organized along multiple axes.

Automated Identification of Diabetic Retinopathy Using Deep Learning.

PURPOSE: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The objective of this study was to develop robust diagnostic technology to automate DR screening. Referral of eyes with DR to an ophthalmologist for further evaluation and treatment would aid in reducing the rate of vision loss, enabling timely and accurate diagnoses. DESIGN: We developed and evaluated a data-driven deep learning algorithm as a novel diagnostic tool for automated DR detection. The algorithm processed color fundus images and classified them as healthy (no retinopathy) or having DR, identifying relevant cases for medical referral. METHODS: A total of 75 137 publicly available fundus images from diabetic patients were used to train and test an artificial intelligence model to differentiate healthy fundi from those with DR. A panel of retinal specialists determined the ground truth for our data set before experimentation. We also tested our model using the public MESSIDOR 2 and E-Ophtha databases for external validation. Information learned in our automated method was visualized readily through an automatically generated abnormality heatmap, highlighting subregions within each input fundus image for further clinical review. MAIN OUTCOME MEASURES: We used area under the receiver operating characteristic curve (AUC) as a metric to measure the precision-recall trade-off of our algorithm, reporting associated sensitivity and specificity metrics on the receiver operating characteristic curve. RESULTS: Our model achieved a 0.97 AUC with a 94% and 98% sensitivity and specificity, respectively, on 5-fold cross-validation using our local data set. Testing against the independent MESSIDOR 2 and E-Ophtha databases achieved a 0.94 and 0.95 AUC score, respectively. CONCLUSIONS: A fully data-driven artificial intelligence-based grading algorithm can be used to screen fundus photographs obtained from diabetic patients and to identify, with high reliability, which cases should be referred to an ophthalmologist for further evaluation and treatment. The implementation of such an algorithm on a global basis could reduce drastically the rate of vision loss attributed to DR.

Fully Automated Prediction of Geographic Atrophy Growth Using Quantitative Spectral-Domain Optical Coherence Tomography Biomarkers.

PURPOSE: To develop a predictive model based on quantitative characteristics of geographic atrophy (GA) to estimate future potential regions of GA growth. DESIGN: Progression study and predictive model. PARTICIPANTS: One hundred eighteen spectral-domain (SD) optical coherence tomography (OCT) scans of 38 eyes in 29 patients. METHODS: Imaging features of GA quantifying its extent and location, as well as characteristics at each topographic location related to individual retinal layer thickness and reflectivity, the presence of pathologic features (like reticular pseudodrusen or loss of photoreceptors), and other known risk factors of GA growth, were extracted automatically from 118 SD OCT scans of 38 eyes from 29 patients collected over a median follow-up of 2.25 years. We developed and evaluated a model to predict the magnitude and location of GA growth at given future times using the quantitative features as predictors in 3 possible scenarios. MAIN OUTCOME MEASURES: Potential regions of GA growth. RESULTS: In descending order of out-of-bag feature importance, the most predictive SD OCT biomarkers for predicting the future regions of GA growth were thickness loss of bands 11 through 14 (5.66), reflectivity of bands 11 and 12 (5.37), thickness of reticular pseudodrusen (5.01), thickness of bands 5 through 11 (4.82), reflectivity of bands 7 through 11 (4.78), GA projection image (4.73), increased minimum retinal intensity map (4.59), and GA eccentricity (4.49). The predicted GA regions in the 3 tested scenarios resulted in a Dice index mean ± standard deviation of 0.81±0.12, 0.84±0.10, and 0.87±0.06, respectively, when compared with the observed ground truth. Considering only the regions without evidence of GA at baseline, predicted regions of future GA growth showed relatively high Dice indices of 0.72±0.18, 0.74±0.17, and 0.72±0.22, respectively. Predictions and actual values of GA growth rate and future GA involvement in the central fovea showed high correlations. CONCLUSIONS: Experimental results demonstrated the potential of our predictive model to predict future regions where GA is likely to grow and to identify the most discriminant early indicator (thickness loss of bands 11 through 14) of regions susceptible to GA growth.

PROSPECTIVE TRIAL OF ENDOGENOUS FUNGAL ENDOPHTHALMITIS AND CHORIORETINITIS RATES, CLINICAL COURSE, AND OUTCOMES IN PATIENTS WITH FUNGEMIA.

PURPOSE: Former studies have found rates of endogenous endophthalmitis ranging from 0% to 37% in patients with fungemia. This study sought to prospectively determine the rate and risk factors for endogenous chorioretinitis and endophthalmitis in patients with fungemia. METHODS: A prospective cohort study was performed of consecutive adult inpatients at a single site from 2010 to 2013 of patients with positive blood cultures for fungus. One hundred and nineteen pieces of information were gathered for each patient. RESULTS: A total of 125 patients were enrolled in the study with 7 positive cases of chorioretinitis for a rate of 5.6%. Of these positive cases, 2 patients had endophthalmitis for a rate of 1.6%. Two patients who had a negative initial examination subsequently had a positive examination; 57% of the chorioretinitis patients who could report symptoms were asymptomatic, 57% of the chorioretinitis patients died, and 32% of negative cases died. Prolonged hospitalization, altered mental status, total parenteral nutrition, and gastrointestinal surgery were protective on univariate but not multivariate analysis. CONCLUSION: Despite modern antifungal therapy, fungal chorioretinitis and endophthalmitis continue to occur in patients with positive fungal cultures. Two dilated ophthalmic examinations should still be considered even in asymptomatic patients with fungemia.

AFLIBERCEPT FOR THE TREATMENT OF RETINAL PIGMENT EPITHELIAL DETACHMENTS.

PURPOSE: To compare anatomical and visual acuity outcomes of eyes with persistent pigment epithelial detachments (PEDs) secondary to exudative age-related macular degeneration despite ranibizumab or bevacizumab treatment. METHODS: After institutional review board approval, 40 eyes with PEDs switched from ranibizumab or bevacizumab to intravitreal aflibercept were compared for logMAR visual acuity, central subfield thickness on spectral domain optical coherence tomography, and PED height. Using paired t-tests, these parameters at baseline, after 3 consecutive injections, and 1 year after the switch were compared. RESULTS: Baseline visions of 20/61 ± 3.99 lines declined after 3 injections with aflibercept by 0.39 ± 2.43 lines (P = 0.32) and continued to fall after 1 year by 1.27 ± 3.48 lines (P = 0.03). Central subfield thickness was reduced after 3 injections (9.1 ± 52.0 µm, P = 0.27) and after 1 year (24.4 ± 55.3 µm, P = 0.01). The height of PEDs decreased by 31.7 ± 71.53 µm (P = 0.008) after 3 injections and by 47.81 ± 77.94 µm (P < 0.001) after 1 year. CONCLUSION: Switching to aflibercept from ranibizumab or bevacizumab resulted in a reduction in the height of PED and central subfield thickness, but a trend toward worse visual acuity 1 year after the switch.

THINKING LEAN: Improving Vitreoretinal Clinic Efficiency by Decentralizing Optical Coherence Tomography.

PURPOSE: Patients in vitreoretinal clinic have long wait times that could be reduced by improving the efficiency of patient flow. The objective of this study was to determine whether decentralizing optical coherence tomography (OCT) into the technicians' room would reduce patient wait times and improve clinic efficiency. METHODS: Randomized, single-center, clinical trial for 1 month without follow-up at Byers Eye Institute at Stanford. Subjects were return patients of three vitreoretinal specialists in March 2013. The intervention consisted of decentralizing OCT devices from the central photography suite into the technician screening rooms. Total clinic times and total wait times throughout subject appointments were recorded and compared with the control group (centralized photography suite). Secondary outcomes included frequency of injections, procedures, and primary diagnosis codes. RESULTS: Decentralized OCT reduced patient wait times by 74% and reduced total clinic appointment time by 36%. Subjects in the intervention arm experienced significantly reduced total wait time (mean difference = 15.9 minutes, P < 0.0001) and total time in clinic (mean difference = 22.9 minutes, P < 0.0001). CONCLUSION: Decentralized OCT represents the application of lean process concepts to improve vitreoretinal clinic efficiency. Decentralized OCT reduced both the total wait time and total time in clinic for return patients in a vitreoretinal clinic.

Automated segmentation of optic disc in SD-OCT images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection.

Glaucoma is one of the most common causes of blindness worldwide. Early detection of glaucoma is traditionally based on assessment of the cup-to-disc (C/D) ratio, an important indicator of structural changes to the optic nerve head. Here, we present an automated optic disc segmentation algorithm in 3-D spectral domain optical coherence tomography (SD-OCT) volumes to quantify this ratio. The proposed algorithm utilizes a two-stage strategy. First, it detects the neural canal opening (NCO) by finding the points with maximum curvature on the retinal pigment epithelium (RPE) boundary with a spatial correlation smoothness constraint on consecutive B-scans, and it approximately locates the coarse disc margin in the projection image using convex hull fitting. Then, a patch searching procedure using a probabilistic support vector machine (SVM) classifier finds the most likely patch with the NCO in its center in order to refine the segmentation result. Thus, a reference plane can be determined to calculate the C/D radio. Experimental results on 42 SD-OCT volumes from 17 glaucoma patients demonstrate that the proposed algorithm can achieve high segmentation accuracy and a low C/D ratio evaluation error. The unsigned border error for optic disc segmentation and the evaluation error for C/D ratio comparing with manual segmentation are 2.216 ± 1.406 pixels (0.067 ± 0.042 mm) and 0.045 ± 0.033, respectively.

Application of improved homogeneity similarity-based denoising in optical coherence tomography retinal images.

Image denoising is a fundamental preprocessing step of image processing in many applications developed for optical coherence tomography (OCT) retinal imaging--a high-resolution modality for evaluating disease in the eye. To make a homogeneity similarity-based image denoising method more suitable for OCT image removal, we improve it by considering the noise and retinal characteristics of OCT images in two respects: (1) median filtering preprocessing is used to make the noise distribution of OCT images more suitable for patch-based methods; (2) a rectangle neighborhood and region restriction are adopted to accommodate the horizontal stretching of retinal structures when observed in OCT images. As a performance measurement of the proposed technique, we tested the method on real and synthetic noisy retinal OCT images and compared the results with other well-known spatial denoising methods, including bilateral filtering, five partial differential equation (PDE)-based methods, and three patch-based methods. Our results indicate that our proposed method seems suitable for retinal OCT imaging denoising, and that, in general, patch-based methods can achieve better visual denoising results than point-based methods in this type of imaging, because the image patch can better represent the structured information in the images than a single pixel. However, the time complexity of the patch-based methods is substantially higher than that of the others.

Multimodal imaging and multifocal electroretinography demonstrate autosomal recessive Stargardt disease may present like occult macular dystrophy.

PURPOSE: To describe multimodal imaging and electrophysiologic characteristics of an unusual subset of patients with genetically confirmed autosomal recessive Stargardt disease (STGD1) who exhibited a central form of cone dysfunction resembling occult macular dystrophy that preceded the development of lipofuscin flecks, atrophy of retinal pigment epithelium (RPE), or full-field electroretinography abnormalities. METHODS: Retrospective, observational descriptive case series. RESULTS: Five patients with compound heterozygous ABCA4 mutations presented with bilateral visual acuity reduction, normal-appearing fundi, and blocked choroidal fluorescence on fluorescein angiography. One sibling each of two probands with identical genotypes was also included for analysis. Full-field electroretinography testing was normal in all patients, but multifocal electroretinography demonstrated centripetally depressed amplitudes exceeding areas of fundus autofluorescence, infrared imaging, and spectral domain optical coherence tomography abnormalities. Spectral domain optical coherence tomography initially revealed disruption of the inner segment ellipsoid band accompanying an ovoid hypofluorescent foveolar lesion. Progression to later stages was accompanied by the loss of the foveal photoreceptor outer segments, creating foveal cavitation with preservation of the RPE. Fundus autofluorescence and infrared imaging demonstrated corresponding bull's eye lesions. Over time, the foveal potential space on spectral domain optical coherence tomography collapsed, and three patients developed RPE atrophy and visible lipofuscin flecks. The flecks were detectable by fundus autofluorescence and infrared imaging earlier than by biomicroscopy. From these findings, a staging system for this subset of Stargardt disease presenting with central cone dysfunction was developed and presented herein. CONCLUSION: Autosomal recessive Stargardt disease may present as a central cone dysfunction syndrome before the development of lipofuscin flecks, atrophy of RPE, or full-field electroretinography abnormalities. If emerging therapies for Stargardt disease succeed, early recognition and treatment of patients with preserved foveal photoreceptor and RPE cell bodies may yield a more favorable visual prognosis.

A false color fusion strategy for drusen and geographic atrophy visualization in optical coherence tomography images.

PURPOSE: To display drusen and geographic atrophy (GA) in a single projection image from three-dimensional spectral domain optical coherence tomography images based on a novel false color fusion strategy. METHODS: We present a false color fusion strategy to combine drusen and GA projection images. The drusen projection image is generated with a restricted summed-voxel projection (axial sum of the reflectivity values in a spectral domain optical coherence tomography cube, limited to the region where drusen is present). The GA projection image is generated by incorporating two GA characteristics: bright choroid and thin retina pigment epithelium. The false color fusion method was evaluated in 82 three-dimensional optical coherence tomography data sets obtained from 7 patients, for which 2 readers independently identified drusen and GA as the gold standard. The mean drusen and GA overlap ratio was used as the metric to determine accuracy of visualization of the proposed method when compared with the conventional summed-voxel projection, (axial sum of the reflectivity values in the complete spectral domain optical coherence tomography cube) technique and color fundus photographs. RESULTS: Comparative results demonstrate that the false color image is more effective in displaying drusen and GA than summed-voxel projection and CFP. The mean drusen/GA overlap ratios based on the conventional summed-voxel projection method, color fundus photographs, and the false color fusion method were 6.4%/100%, 64.1%/66.7%, and 85.6%/100%, respectively. CONCLUSION: The false color fusion method was more effective for simultaneous visualization of drusen and GA than the conventional summed-voxel projection method and color fundus photographs, and it seems promising as an alternative method for visualizing drusen and GA in the retinal fundus, which commonly occur together and can be confusing to differentiate without methods such as this proposed one.

An improved optical coherence tomography-derived fundus projection image for drusen visualization.

PURPOSE: To develop and evaluate an improved method of generating en face fundus images from three-dimensional optical coherence tomography images which enhances the visualization of drusen. METHODS: We describe a novel approach, the restricted summed-voxel projection (RSVP), to generate en face projection images of the retinal surface combined with an image processing method to enhance drusen visualization. The RSVP approach is an automated method that restricts the projection to the retinal pigment epithelium layer neighborhood. Additionally, drusen visualization is improved through an image processing technique that fills drusen with bright pixels. The choroid layer is also excluded when creating the RSVP to eliminate bright pixels beneath drusen that could be confused with drusen when geographic atrophy is present. The RSVP method was evaluated in 46 patients and 3-dimensional optical coherence tomography data sets were obtained from 8 patients, for which 2 readers independently identified drusen as the gold standard. The mean drusen overlap ratio was used as the metric to determine the accuracy of visualization of the RSVP method when compared with the conventional summed-voxel projection technique. RESULTS: Comparative results demonstrate that the RSVP method was more effective than the conventional summed-voxel projection in displaying drusen and retinal vessels, and was more useful in detecting drusen. The mean drusen overlap ratios based on the conventional summed-voxel projection method and the RSVP method were 2.1% and 89.3%, respectively. CONCLUSION: The RSVP method was more effective for drusen visualization than the conventional summed-voxel projection method, and it may be useful for macular assessment in patients with nonexudative age-related macular degeneration.

Retinal Detachment After Macular Hole Repair in Alport Syndrome.

Alport syndrome is characterized by type IV collagen network disruptions leading to renal, auditory, and ocular manifestations. This case report details a 24-year-old man with Alport syndrome who developed a rhegmatogenous retinal detachment following macular hole repair. The patient underwent a successful vitrectomy and internal limiting membrane peel for macular hole repair but returned with vision loss due to retinal detachment five weeks later, which necessitated a combined scleral buckle and vitrectomy. This is the first case describing rhegmatogenous retinal detachment post-macular hole repair in Alport syndrome. [Ophthalmic Surg Lasers Imaging Retina 2024;55:289-292.].

Acute Subretinal Fluid Accumulation Induced by Futibatinib Therapy for Malignant Metastatic Breast Cancer.

Futibatinib is an irreversible inhibitor of fibroblast growth factor receptors and is currently the subject of phase II clinical trials for the treatment of metastatic carcinomas. We report a case of a 59-year-old woman with metastatic malignant breast cancer who developed acute symptomatic subretinal fluid (SRF) accumulation after two weeks of futibatinib therapy. The SRF resolved within two weeks after futibatinib cessation. The medication was subsequently restarted at a lower dose, and SRF recurred within two weeks. To our knowledge, this is the first case depicting rapidly reversible SRF accumulation with the use of futibatinib in a real-world clinical setting. [Ophthalmic Surg Lasers Imaging Retina 2024;55:109-111.].

Quantitative Characterization of Retinal Features in Translated OCTA.

Purpose: This study explores the feasibility of using generative machine learning (ML) to translate Optical Coherence Tomography (OCT) images into Optical Coherence Tomography Angiography (OCTA) images, potentially bypassing the need for specialized OCTA hardware. Methods: The method involved implementing a generative adversarial network framework that includes a 2D vascular segmentation model and a 2D OCTA image translation model. The study utilizes a public dataset of 500 patients, divided into subsets based on resolution and disease status, to validate the quality of TR-OCTA images. The validation employs several quality and quantitative metrics to compare the translated images with ground truth OCTAs (GT-OCTA). We then quantitatively characterize vascular features generated in TR-OCTAs with GT-OCTAs to assess the feasibility of using TR-OCTA for objective disease diagnosis. Result: TR-OCTAs showed high image quality in both 3 and 6 mm datasets (high-resolution, moderate structural similarity and contrast quality compared to GT-OCTAs). There were slight discrepancies in vascular metrics, especially in diseased patients. Blood vessel features like tortuosity and vessel perimeter index showed a better trend compared to density features which are affected by local vascular distortions. Conclusion: This study presents a promising solution to the limitations of OCTA adoption in clinical practice by using vascular features from TR-OCTA for disease detection. Translation relevance: This study has the potential to significantly enhance the diagnostic process for retinal diseases by making detailed vascular imaging more widely available and reducing dependency on costly OCTA equipment.