Retinal axial motion analysis and implications for real-time correction in human retinal imaging

High-resolution ophthalmic imaging devices including spectral-domain and full-field optical coherence tomography (SDOCT and FFOCT) are adversely affected by the presence of continuous involuntary retinal axial motion. Here, we thoroughly quantify and characterize retinal axial motion with both high temporal resolution (200,000 A-scans/s) and high axial resolution (4.5 um), recorded over a typical data acquisition duration of 3 s with an SDOCT device over 14 subjects. We demonstrate that although breath-holding can help decrease large-and-slow drifts, it increases small-and-fast fluctuations, which is not ideal when motion compensation is desired. Finally, by simulating the action of an axial motion stabilization control loop, we show that a loop rate of 1.2 kHz is ideal to achieve 100% robust clinical in-vivo retinal imaging.

Advances and Current Clinical Applications of Anterior Segment Optical Coherence Tomography Angiography

Optical coherence tomography angiography (OCTA) is the most relevant evolution based on optical coherence tomography (OCT). OCTA can present ocular vasculature, show detailed morphology for assessment, and quantify vessel parameters without intravenous dye agent. Research on the anterior segment OCTA (AS-OCTA) is only in its initial phase, and its advances in clinical diagnosis and treatment efficacy evaluations require a detailed comparison to traditional imaging methods. In this review of AS-OCTA, we summarize its technical features, imaging advances, current clinical applications in various eye diseases, as well as its limitations and potential future directions. AS-OCTA offers potential advantages in ophthalmic imaging, and with further development it could become a common tool in the near future.

Incidence of Referable Retinal Disease in Diabetic Patients at a Primary Care Practice

Purpose: This work tests the feasibility of remote ophthalmic imaging to identify referable retinal abnormalities and assesses the effectiveness of color fundus photography (CFP) vs optical coherence tomography (OCT) for this purpose. Methods: This prospective, nonrandomized study included 633 patients with diabetes at Duke Primary Care. Undilated patients underwent screening with CFP and OCT camera (MaestroCare, Topcon). Images were graded independently for interpretability and the presence of predetermined retinal disease. Retinal disease was classified as diabetic retinopathy (DR) referable to a retina specialist or incidental findings referable to either a retina specialist or a general ophthalmologist, depending on severity. Results: Mean (SD) age of screened patients was 66 (13) years, and 49% were women. The average glycated hemoglobin A1c level was 7.6 % (SD, 1.7%), and 30% of the patients were on insulin. The average duration of diabetes was 5.9 (SD, 7.3) years. Remote images from OCT were significantly more interpretable than CFP (98% vs 83%, respectively; P < .001). Referral rates were 9% for DR and 28% for incidental findings. Among patients with DR, OCT and CFP were helpful in 58% and 87% of cases, respectively ( P < .001). Conclusions: Remote diagnosis of ophthalmic imaging at the point of service may allow for early identification of retinal disease and timely referral and treatment. Our approach showed that OCT had significantly better interpretability, while CFP was more helpful in identifying DR. These findings may be important when choosing the screening device in a specific context.

Long-Term Clinical and Multimodal Imaging Findings in Patients with Disseminated Mycobacterium Chimaera Infection

Background: To analyze long-term ophthalmic clinical and multimodal imaging findings of disseminated Mycobacterium (M.) chimaera infection after cardiothoracic surgery among the Swiss Cohort. Methods: Systemic and multimodal ophthalmic imaging and clinical findings including rate of recurrence were reviewed and correlated to a previously proposed classification system of choroidal lesions and classification of ocular disease. Main Outcomes Measures: long-term clinical and multimodal ocular imaging findings of M. chimaera. Results: Twelve patients suffering from systemic infection from M. chimaera were included. Mean age at the first ophthalmic examination was 59 years (range from 48 to 66 years). Mean duration of the follow-up was 22.63 ± 17.8 months. All patients presented with bilateral chorioretinal lesions at baseline; 5 patients had additional signs, including optic disc swelling (2), choroidal neovascularization (1), retinal neovascularization (1) and cilioretinal vascular occlusion (1). Four recurrence events after discontinuation or adjustment of the antibiotic treatment were observed. Progressive choroiditis was seen in 5 patients under treatment, 4 of them deceased. Conclusions: Expertise from ophthalmologists is not only relevant but also critical for the assessment of the adverse drug effect of antimycobacterial treatment along with monitoring therapeutic response and identifying recurrences.

Clinical Characterization of Korean Patients with Pseudoxanthoma Elasticum and Angioid Streaks

This study aimed to characterize Korean patients with pseudoxanthoma elasticum (PXE) presenting with angioid streaks. Retinal phenotypes were longitudinally evaluated by multimodal ophthalmic imaging, and targeted gene panel sequencing for inherited retinal diseases was conducted. Seven subjects from unrelated families (median age, 51.2 years) were enrolled and followed for a median of 3.2 years. Four asymptomatic patients were significantly younger than three symptomatic patients with decreased visual acuity at presentation (mean age; 38.1 vs. 61.5 years, p = 0.020). The asymptomatic patients maintained good vision (20/32 or better) and had no choroidal neovascularization (CNV) over the observation period. The symptomatic patients showed additional reduction in visual acuity and bilateral CNV occurrence during the longitudinal follow-up. Pathogenic ABCC6 variants were identified in all patients, leading to a diagnosis of PXE. Heterozygous monoallelic variants were identified in four patients and compound heterozygous variants were detected in three patients. Nine ABCC6 variants were identified, including one novel variant, c.2035G>T [p.Glu679Ter]. This is the first genetic study of Korean patients with PXE.

A Multimodal Imaging–Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study

Background Retinal vascular diseases, including diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), myopic choroidal neovascularization (mCNV), and branch and central retinal vein occlusion (BRVO/CRVO), are considered vision-threatening eye diseases. However, accurate diagnosis depends on multimodal imaging and the expertise of retinal ophthalmologists. Objective The aim of this study was to develop a deep learning model to detect treatment-requiring retinal vascular diseases using multimodal imaging. Methods This retrospective study enrolled participants with multimodal ophthalmic imaging data from 3 hospitals in Taiwan from 2013 to 2019. Eye-related images were used, including those obtained through retinal fundus photography, optical coherence tomography (OCT), and fluorescein angiography with or without indocyanine green angiography (FA/ICGA). A deep learning model was constructed for detecting DME, nAMD, mCNV, BRVO, and CRVO and identifying treatment-requiring diseases. Model performance was evaluated and is presented as the area under the curve (AUC) for each receiver operating characteristic curve. Results A total of 2992 eyes of 2185 patients were studied, with 239, 1209, 1008, 211, 189, and 136 eyes in the control, DME, nAMD, mCNV, BRVO, and CRVO groups, respectively. Among them, 1898 eyes required treatment. The eyes were divided into training, validation, and testing groups in a 5:1:1 ratio. In total, 5117 retinal fundus photos, 9316 OCT images, and 20,922 FA/ICGA images were used. The AUCs for detecting mCNV, DME, nAMD, BRVO, and CRVO were 0.996, 0.995, 0.990, 0.959, and 0.988, respectively. The AUC for detecting treatment-requiring diseases was 0.969. From the heat maps, we observed that the model could identify retinal vascular diseases. Conclusions Our study developed a deep learning model to detect retinal diseases using multimodal ophthalmic imaging. Furthermore, the model demonstrated good performance in detecting treatment-requiring retinal diseases.