Visualizing features with wide-field volumetric OCT angiography.

Optical coherence tomography (OCT) and its extension OCT angiography (OCTA) have become essential clinical imaging modalities due to their ability to provide depth-resolved angiographic and tissue structural information non-invasively and at high resolution. Within a field of view, the anatomic detail available is sufficient to identify several structural and vascular pathologies that are clinically relevant for multiple prevalent blinding diseases, including age-related macular degeneration (AMD), diabetic retinopathy (DR), and vein occlusions. The main limitation in contemporary OCT devices is that this field of view is limited due to a fundamental trade-off between system resolution/sensitivity, sampling density, and imaging window dimensions. Here, we describe a swept-source OCT device that can capture up to a 12 × 23-mm field of view in a single shot and show that it can identify conventional pathologic features such as non-perfusion areas outside of conventional fields of view. We also show that our approach maintains sensitivity sufficient to visualize novel features, including choriocapillaris morphology beneath the macula and macrophage-like cells at the inner limiting membrane, both of which may have implications for disease.

THREE-DIMENSIONAL QUANTIFICATION OF INTRARETINAL CYSTOID SPACES ASSOCIATED WITH FULL-THICKNESS MACULAR HOLE.

PURPOSE: To evaluate intraretinal cystoid spaces in patients with idiopathic macular hole (MH). METHODS: Retrospective cohort study included consecutive patients with full-thickness MH who underwent successful MH surgery and 12 months of follow-up. Custom software was applied to preoperative optical coherence tomography scans to generate fluid volume. Inner fluid volume was defined as cystoid spaces in the inner nuclear layer, and outer fluid volume was defined as cystoid spaces in Henle fiber layer of the outer nuclear layer. RESULTS: Thirty-nine eyes from 39 participants were included. Postoperative 12-month visual acuity correlated with both inner fluid volume and minimum MH size (both P < 0.05) but not outer fluid volume. Inner fluid volume positively correlated with minimum MH size ( P = 0.0003). After accounting for minimum MH size with multivariable analysis, inner fluid volume effect on VA remained significant ( P = 0.025). After dividing inner fluid volume into tertiles, mean baseline visual acuity was 20/50 in eyes with small inner fluid volume, and was 20/125 in eyes with large inner fluid volume ( P = 0.0039). Mean postoperative 12-month visual acuity was 20/20 in eyes with small inner fluid volume compared with 20/32 in eyes with large inner fluid volume ( P = 0.019). CONCLUSION: Increased inner fluid volume was associated with worse postoperative VA.

DETECTION OF CLINICALLY UNSUSPECTED RETINAL NEOVASCULARIZATION WITH WIDE-FIELD OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY.

PURPOSE: To evaluate wide-field optical coherence tomography angiography (OCTA) for detection of clinically unsuspected neovascularization (NV) in diabetic retinopathy (DR). METHODS: This prospective observational single-center study included adult patients with a clinical diagnosis of nonproliferative DR. Participants underwent a clinical examination, standard 7-field color photography, and OCTA with commercial and prototype swept-source devices. The wide-field OCTA was achieved by montaging five 6 × 10-mm scans from a prototype device into a 25 × 10-mm image and three 6 × 6-mm scans from a commercial device into a 15 × 6-mm image. A masked grader determined the retinopathy severity from color photographs. Two trained readers examined conventional and wide-field OCTA images for the presence of NV. RESULTS: Of 27 participants, photographic grading found 13 mild, 7 moderate, and 7 severe nonproliferative DR. Conventional 6 × 6-mm OCTA detected NV in 2 eyes (7%) and none with 3 × 3-mm scans. Both prototype and commercial wide-field OCTA detected NV in two additional eyes. The mean area of NV was 0.38 mm (range 0.17-0.54 mm). All eyes with OCTA-detected NV were photographically graded as severe nonproliferative DR. CONCLUSION: Wide-field OCTA can detect small NV not seen on clinical examination or color photographs and may improve the clinical evaluation of DR.

Medicare Incentive Payments to United States Ophthalmologists for Use of Electronic Health Records: 2011-2016.

PURPOSE: To investigate ophthalmologists' rate of attestation to meaningful use (MU) of their electronic health record (EHR) systems in the Medicare EHR Incentive Program and their continuity and success in receiving payments in comparison with other specialties. DESIGN: Administrative database study. PARTICIPANTS: Eligible professionals participating in the Medicare EHR Incentive Program. METHODS: Based on publicly available data sources, subsets of payment and attestation data were created for ophthalmologists and for other specialties. The number of eligible professionals attesting was determined using the attestation data for each year and stage of the program. The proportion of attestations by EHR vendor was calculated using all attestations for each vendor. MAIN OUTCOME MEASURES: Numbers of ophthalmologists attesting by year and stage of the Medicare EHR Incentive Program, incentive payments, and number of attestations by EHR vendor. RESULTS: In the peak year of participation, 51.6% of ophthalmologists successfully attested to MU, compared with 37.1% of optometrists, 50.2% of dermatologists, 54.5% of otolaryngologists, and 64.4% of urologists. Across the 6 years of the program, ophthalmologists received an average of $17 942 in incentive payments compared with $11 105 for optometrists, $16 617 for dermatologists, $20 203 for otolaryngologists, and $23 821 for urologists. Epic and Nextgen were the most frequently used EHRs for attestation by ophthalmologists. CONCLUSIONS: Ophthalmology as a specialty performed better than optometry and dermatology, but worse than otolaryngology and urology, in terms of the proportion of eligible professionals attesting to MU of EHRs. Ophthalmologists were more likely to remain in the program after their initial year of attestation compared with all eligible providers. The top 4 EHR vendors accounted for 50% of attestations by ophthalmologists.

Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye.

Retinal vascular diseases are important causes of vision loss. A detailed evaluation of the vascular abnormalities facilitates diagnosis and treatment in these diseases. Optical coherence tomography (OCT) angiography using the highly efficient split-spectrum amplitude decorrelation angiography algorithm offers an alternative to conventional dye-based retinal angiography. OCT angiography has several advantages, including 3D visualization of retinal and choroidal circulations (including the choriocapillaris) and avoidance of dye injection-related complications. Results from six illustrative cases are reported. In diabetic retinopathy, OCT angiography can detect neovascularization and quantify ischemia. In age-related macular degeneration, choroidal neovascularization can be observed without the obscuration of details caused by dye leakage in conventional angiography. Choriocapillaris dysfunction can be detected in the nonneovascular form of the disease, furthering our understanding of pathogenesis. In choroideremia, OCT's ability to show choroidal and retinal vascular dysfunction separately may be valuable in predicting progression and assessing treatment response. OCT angiography shows promise as a noninvasive alternative to dye-based angiography for highly detailed, in vivo, 3D, quantitative evaluation of retinal vascular abnormalities.

Optical Coherence Tomography Reflective Drusen Substructures Predict Progression to Geographic Atrophy in Age-related Macular Degeneration.

PURPOSE: Structural and compositional heterogeneity within drusen comprising lipids, carbohydrates, and proteins have been previously described. We sought to detect and define phenotypic patterns of drusen heterogeneity in the form of optical coherence tomography-reflective drusen substructures (ODS) and examine their associations with age-related macular degeneration (AMD)-related features and AMD progression. DESIGN: Retrospective analysis in a prospective study. PARTICIPANTS: Patients with intermediate AMD (n = 349) enrolled in the multicenter Age-Related Eye Disease Study 2 (AREDS2) ancillary spectral-domain optical coherence tomography (SD OCT) study. METHODS: Baseline SD OCT scans of 1 eye per patient were analyzed for the presence of ODS. Cross-sectional and longitudinal associations of ODS presence with AMD-related features visible on SD OCT and color photographs, including drusen volume, geographic atrophy (GA), and preatrophic features, were evaluated for the entire macular region. Similar associations were also made locally within a 0.5-mm-diameter region around individual ODS and corresponding control region without ODS in the same eye. MAIN OUTCOME MEASURES: Preatrophy SD OCT changes and GA, central GA, and choroidal neovascularization (CNV) from color photographs. RESULTS: Four phenotypic subtypes of ODS were defined: low reflective cores, high reflective cores, conical debris, and split drusen. Among the 349 participants, there were 307 eligible eyes and 74 (24%) had at least 1 ODS. The ODS at baseline were associated with (1) greater macular drusen volume at baseline (P < 0.001), (2) development of preatrophic changes at year 2 (P = 0.001-0.01), and (3) development of macular GA (P = 0.005) and preatrophic changes at year 3 (P = 0.002-0.008), but not development of CNV. The ODS at baseline in a local region were associated with (1) presence of preatrophy changes at baseline (P = 0.02-0.03) and (2) development of preatrophy changes at years 2 and 3 within the region (P = 0.008-0.05). CONCLUSIONS: Optical coherence tomography-reflective drusen substructures are optical coherence tomography-based biomarkers of progression to GA, but not to CNV, in eyes with intermediate AMD. Optical coherence tomography-reflective drusen substructures may be a clinical entity helpful in monitoring AMD progression and informing mechanisms in GA pathogenesis.

OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES OF DIABETIC RETINOPATHY.

PURPOSE: To describe the optical coherence tomography angiography features of diabetic retinopathy. METHODS: Using a 70 kHz optical coherence tomography and the split-spectrum amplitude decorrelation angiography algorithm, 6 mm × 6 mm 3-dimensional angiograms of the macula of 4 patients with diabetic retinopathy were obtained and compared with fluorescein angiography for features cataloged by the Early Treatment of Diabetic Retinopathy Study. RESULTS: Optical coherence tomography angiography detected enlargement and distortion of the foveal avascular zone, retinal capillary dropout, and pruning of arteriolar branches. Areas of capillary loss obscured by fluorescein leakage on fluorescein angiography were more clearly defined on optical coherence tomography angiography. Some areas of focal leakage on fluorescein angiography that were thought to be microaneurysms were found to be small tufts of neovascularization that extended above the inner limiting membrane. CONCLUSION: Optical coherence tomography angiography does not show leakage but can better delineate areas of capillary dropout and detect early retinal neovascularization. This new noninvasive angiography technology may be useful for routine surveillance of proliferative and ischemic changes in diabetic retinopathy.

DETECTION OF NONEXUDATIVE CHOROIDAL NEOVASCULARIZATION IN AGE-RELATED MACULAR DEGENERATION WITH OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY.

PURPOSE: To evaluate eyes with age-related macular degeneration and high-risk characteristics for choroidal neovascularization (CNV) with optical coherence tomographic (OCT) angiography to determine whether earlier detection of CNV is possible. METHODS: Eyes with drusen, pigmentary changes, and with CNV in the fellow eye were scanned with a 70-kHz spectral domain OCT system (Optovue RTVue-XR Avanti). The split-spectrum amplitude-decorrelation angiography (SSADA) algorithm was used to distinguish blood flow from static tissue. Two masked graders reviewed scans for CNV, defined as flow in the outer retinal/sub-RPE slab. Choroidal neovascularization flow area repeatability and between-grader reproducibility were calculated. RESULTS: Of 32 eyes, 2 (6%) were found to have Type 1 CNV with OCT angiography. The lesions were not associated with leakage on fluorescein angiography or fluid on OCT. One case was followed for 8 months without treatment, and the CNV flow area enlarged slightly without fluid buildup on OCT or vision loss. Between-grader reproducibility of the CNV flow area was 9.4% (coefficient of variation) and within-visit repeatability was 5.2% (pooled coefficient of variation). CONCLUSION: Optical coherence tomographic angiography can detect the presence of nonexudative CNV, lesions difficult to identify with fluorescein angiography and OCT. Further study is needed to understand the significance and natural history of these lesions.

Bias Reduction Practices in Underrepresented Groups in Ophthalmology Resident Recruitment.

Importance: Best recruitment practices for increasing diversity are well established, but the adoption and impact of these practices in ophthalmology residency recruitment are unknown. Objective: To describe the adoption of bias reduction practices in groups underrepresented in ophthalmology (URiO) residency recruitment and determine which practices are effective for increasing URiO residents. Design, Setting, and Participants: This cross-sectional survey study used an 18-item questionnaire included in the online survey of the Association of University Professors in Ophthalmology (AUPO) Residency Program Directors. Data collection occurred from July 2022 to December 2022. The data were initially analyzed on January 16, 2023. Participants included residency program directors (PDs) in the AUPO PD listserv database. Main Outcomes and Measures: Descriptive analysis of resident selection committee approaches, evaluation of applicant traits, and use of bias reduction tools. Primary outcome was diversity assessed by presence of at least 1 resident in the last 5 classes who identified as URiO, including those underrepresented in medicine (URiM), lesbian, gay, bisexual, transgender, queer, intersex, and asexual plus, or another disadvantaged background (eg, low socioeconomic status). Multivariate analyses of recruitment practices were conducted to determine which practices were associated with increased URiO and URiM. Results: Among 106 PDs, 65 completed the survey (61.3%). Thirty-nine PDs used an interview rubric (60.0%), 28 used interview standardization (43.0%), 56 provided at least 1 bias reduction tool to their selection committee (86.2%), and 44 used postinterview metrics to assess diversity, equity, and inclusion efforts (67.7%). Application filters, interview standardization, and postinterview metrics were not associated with increased URiO. Multivariate logistic regression analysis showed larger residency class (odds ratio [OR], 1.34; 95% CI, 1.09-1.65; P = .01) and use of multiple selection committee bias reduction tools (OR, 1.47; 95% CI, 1.13-1.92; P = .01) were positively associated with increased URiO, whereas use of interview rubrics (OR, 0.72; 95% CI, 0.59-0.87; P = .001) and placing higher importance of applicant interest in a program (OR, 0.83; 95% CI, 0.75-0.92; P = .02) were negatively associated. URiM analyses showed similar associations. Conclusions and Relevance: Ophthalmology residency interviews are variably standardized. In this study, providing multiple bias reduction tools to selection committees was associated with increased URiO and URiM residents. Prioritizing applicant interest in a program may reduce resident diversity. Interview rubrics, while intended to reduce bias, may inadvertently increase inequity.

The Impact of Documentation Workflow on the Accuracy of the Coded Diagnoses in the Electronic Health Record.

Objective: To determine the impact of documentation workflow on the accuracy of coded diagnoses in electronic health records (EHRs). Design: Cross-sectional study. Participants: All patients who completed visits at the Casey Eye Institute Retina Division faculty clinic between April 7, 2022 and April 13, 2022. Main Outcome Measures: Agreement between coded diagnoses and clinical notes. Methods: We assessed the rate of agreement between the diagnoses in the clinical notes and the coded diagnosis in the EHR using manual review and examined the impact of the documentation workflow on the rate of agreement in an academic retina practice. Results: In 202 visits by 8 physicians, 78% (range, 22%-100%) had an agreement between the coded diagnoses and the clinical notes. When physicians integrated the diagnosis code entry and note composition, the rate of agreement was 87.9% (range, 62%-100%). For those who entered the diagnosis codes separately from writing notes, the agreement was 44.4% (22%-50%, P < 0.0001). Conclusion: The visit-specific agreement between the coded diagnosis and the progress note can vary widely by workflow. The workflow and EHR design may be an important part of understanding and improving the quality of EHR data. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Perfused and Nonperfused Microaneurysms Identified and Characterized by Structural and Angiographic OCT.

PURPOSE: Microaneurysms (MAs) have distinct, oval-shaped, hyperreflective walls on structural OCT, and inconsistent flow signal in the lumen with OCT angiography (OCTA). Their relationship to regional macular edema in diabetic retinopathy (DR) has not been quantitatively explored. DESIGN: Retrospective, cross-sectional study. PARTICIPANTS: A total of 99 participants, including 23 with mild, nonproliferative DR (NPDR), 25 with moderate NPDR, 34 with severe NPDR, and 17 with proliferative DR. METHODS: We obtained 3 × 3-mm scans with a commercial device (Solix, Visionix/Optovue) in 99 patients with DR. Trained graders manually identified MAs and their location relative to the anatomic layers from cross-sectional OCT. Microaneurysms were first classified as perfused if flow signal was present in the OCTA channel. Then, perfused MAs were further classified into fully and partially perfused MAs based on the flow characteristics in en face OCTA. The presence of retinal fluid based on OCT near MAs was compared between perfused and nonperfused types. We also compared OCT-based MA detection to fundus photography (FP)- and fluorescein angiography (FA)-based detection. MAIN OUTCOME MEASURES: OCT-identified MAs can be classified according to colocalized OCTA flow signal into fully perfused, partially perfused, and nonperfused types. Fully perfused MAs may be more likely to be associated with diabetic macular edema (DME) than those without flow. RESULTS: We identified 308 MAs (166 fully perfused, 88 partially perfused, 54 nonperfused) in 42 eyes using OCT and OCTA. Nearly half of the MAs identified in this study straddle the inner nuclear layer and outer plexiform layer. Compared with partially perfused and nonperfused MAs, fully perfused MAs were more likely to be associated with local retinal fluid. The associated fluid volumes were larger with fully perfused MAs compared with other types. OCT/OCTA detected all MAs found on FP. Although not all MAs seen with FA were identified with OCT, some MAs seen with OCT were not visible with FA or FP. CONCLUSIONS: OCT-identified MAs with colocalized flow on OCTA are more likely to be associated with DME than those without flow. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Interpretable Diabetic Retinopathy Diagnosis Based on Biomarker Activation Map.

OBJECTIVE: Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers' decision-making. METHODS: A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans. RESULTS: The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid. CONCLUSION/SIGNIFICANCE: A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.

Early Sign of Retinal Neovascularization Evolution in Diabetic Retinopathy: A Longitudinal OCT Angiography Study.

Purpose: To assess whether the combination of en face OCT and OCT angiography (OCTA) can capture observable, but subtle, structural changes that precede clinically evident retinal neovascularization (RNV) in eyes with diabetic retinopathy (DR). Design: Retrospective, longitudinal study. Participants: Patients with DR that had at least 2 visits. Methods: We obtained wide-field OCTA scans of 1 eye from each participant and generated en face OCT, en face OCTA, and cross-sectional OCTA. We identified eyes with RNV sprouts, defined as epiretinal hyperreflective materials on en face OCT with flow signals breaching the internal limiting membrane on the cross-sectional OCTA without recognizable RNV on en face OCTA and RNV fronds, defined as recognizable abnormal vascular structures on the en face OCTA. We examined the corresponding location from follow-up or previous visits for the presence or progression of the RNV. Main Outcome Measures: The characteristics and longitudinal observation of early signs of RNV. Results: From 71 eyes, we identified RNV in 20 eyes with the combination of OCT and OCTA, of which 13 (65%) were photographically graded as proliferative DR, 6 (30%) severe nonproliferative DR, and 1 (5%) moderate nonproliferative diabetic retinopathy. From these eyes, we identified 38 RNV sprouts and 26 RNV fronds at the baseline. Thirty-four RNVs (53%) originated from veins, 24 (38%) were from intraretinal microabnormalities, and 6 (9%) were from a nondilated capillary bed. At the final visit, 53 RNV sprouts and 30 RNV fronds were detected. Ten eyes (50%) showed progression, defined as having a new RNV lesion or the development of an RNV frond from an RNV sprout. Four (11%) RNV sprouts developed into RNV fronds with a mean interval of 7.0 months. Nineteen new RNV sprouts developed during the follow-up, whereas no new RNV frond was observed outside an identified RNV sprout. The eyes with progression were of younger age (P = 0.014) and tended to be treatment naive (P = 0.07) compared with eyes without progression. Conclusions: Longitudinal observation demonstrated that a combination of en face OCT and cross-sectional OCTA can identify an earlier form of RNV before it can be recognized on en face OCTA. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Multi-Plexus Nonperfusion Area Segmentation in Widefield OCT Angiography Using a Deep Convolutional Neural Network.

Purpose: To train and validate a convolutional neural network to segment nonperfusion areas (NPAs) in multiple retinal vascular plexuses on widefield optical coherence tomography angiography (OCTA). Methods: This cross-sectional study included 202 participants with a full range of diabetic retinopathy (DR) severities (diabetes mellitus without retinopathy, mild to moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR) and 39 healthy participants. Consecutive 6 × 6-mm OCTA scans at the central macula, optic disc, and temporal region in one eye from 202 participants in a clinical DR study were acquired with a 70-kHz OCT commercial system (RTVue-XR). Widefield OCTA en face images were generated by montaging the scans from these three regions. A projection-resolved OCTA algorithm was applied to remove projection artifacts at the voxel scale. A deep convolutional neural network with a parallel U-Net module was designed to detect NPAs and distinguish signal reduction artifacts from flow deficits in the superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP). Expert graders manually labeled NPAs and signal reduction artifacts for the ground truth. Sixfold cross-validation was used to evaluate the proposed algorithm on the entire dataset. Results: The proposed algorithm showed high agreement with the manually delineated ground truth for NPA detection in three retinal vascular plexuses on widefield OCTA (mean ± SD F-score: SVC, 0.84 ± 0.05; ICP, 0.87 ± 0.04; DCP, 0.83 ± 0.07). The extrafoveal avascular area in the DCP showed the best sensitivity for differentiating eyes with diabetes but no retinopathy (77%) from healthy controls and for differentiating DR by severity: DR versus no DR, 77%; referable DR (rDR) versus non-referable DR (nrDR), 79%; vision-threatening DR (vtDR) versus non-vision-threatening DR (nvtDR), 60%. The DCP also showed the best area under the receiver operating characteristic curve for distinguishing diabetes from healthy controls (96%), DR versus no DR (95%), and rDR versus nrDR (96%). The three-plexus-combined OCTA achieved the best result in differentiating vtDR and nvtDR (81.0%). Conclusions: A deep learning network can accurately segment NPAs in individual retinal vascular plexuses and improve DR diagnostic accuracy. Translational Relevance: Using a deep learning method to segment nonperfusion areas in widefield OCTA can potentially improve the diagnostic accuracy of diabetic retinopathy by OCT/OCTA systems.

Electronic health record systems in ophthalmology: impact on clinical documentation.

OBJECTIVE: To evaluate quantitative and qualitative differences in documentation of the ophthalmic examination between paper and electronic health record (EHR) systems. DESIGN: Comparative case series. PARTICIPANTS: One hundred fifty consecutive pairs of matched paper and EHR notes, documented by 3 attending ophthalmologist providers. METHODS: An academic ophthalmology department implemented an EHR system in 2006. Database queries were performed to identify cases in which the same problems were documented by the same provider on different dates, using paper versus EHR methods. This was done for 50 consecutive pairs of examinations in 3 different diseases: age-related macular degeneration (AMD), glaucoma, and pigmented choroidal lesions (PCLs). Quantitative measures were used to compare completeness of documenting the complete ophthalmologic examination, as well as disease-specific critical findings using paper versus an EHR system. Qualitative differences in paper versus EHR documentation were illustrated by selecting representative paired examples. MAIN OUTCOME MEASURES: (1) Documentation score, defined as the number of examination elements recorded for the slit-lamp examination, fundus examination, and complete ophthalmologic examination and for critical clinical findings for each disease. (2) Paired comparison of qualitative differences in paper versus EHR documentation. RESULTS: For all 3 diseases (AMD, glaucoma, PCL), the number of complete examination findings recorded was significantly lower with paper than the EHR system (P ≤ 0.004). Among the 3 individual examination sections (general, slit lamp, fundus) for the 3 diseases, 5 of the 9 possible combinations had significantly lower mean documentation scores with paper than EHR notes. For 2 of the 3 diseases, the number of critical clinical findings recorded was significantly lower using paper versus EHR notes (P ≤ 0.022). All (150/150) paper notes relied on graphical representations using annotated hand-drawn sketches, whereas no (0/150) EHR notes contained drawings. Instead, the EHR systems documented clinical findings using textual descriptions and interpretations. CONCLUSIONS: There were quantitative and qualitative differences in the nature of paper versus EHR documentation of ophthalmic findings in this study. The EHR notes included more complete documentation of examination elements using structured textual descriptions and interpretations, whereas paper notes used graphical representations of findings. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Deep-Learning-Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT.

Purpose: Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies. Design: Cross sectional study. Participants: Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma. Methods: The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis. Main Outcome Measures: The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework. Results: For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02. Conclusions: Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Signal attenuation-compensated projection-resolved OCT angiography.

Projection artifacts are a significant limitation of optical coherence tomographic angiography (OCTA). Existing techniques to suppress these artifacts are sensitive to image quality, becoming less reliable on low-quality images. In this study, we propose a novel signal attenuation-compensated projection-resolved OCTA (sacPR-OCTA) algorithm. In addition to removing projection artifacts, our method compensates for shadows beneath large vessels. The proposed sacPR-OCTA algorithm improves vascular continuity, reduces the similarity of vascular patterns in different plexuses, and removes more residual artifacts compared to existing methods. In addition, the sacPR-OCTA algorithm better preserves flow signal in choroidal neovascular lesions and shadow-affected areas. Because sacPR-OCTA processes the data along normalized A-lines, it provides a general solution for removing projection artifacts agnostic to the platform.