Retinal volume change is a reliable OCT biomarker for disease activity in neovascular AMD

Burchard, Claus von der; Treumer, Felix; Ehlken, Christoph; Koinzer, Stefan; Purtskhvanidze, Konstantine; Tode, Jan; Roider, Johann

doi:10.1007/s00417-018-4040-7

Cited by 18 publications

(14 citation statements)

References 17 publications

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“…There is increasing evidence suggesting a significant relationship between the OCT appearance of the ELM or the EZ and the visual function in different RVD [12][13][14][15]. Different OCT biomarkers have been associated with functional outcomes in patients with age-related macular degeneration (AMD) [12,16]; diabetic macular edema (DME) [15,17]; retinal vein occlusion (RVO) [18]; and uveitis [19].…”

Section: Introductionmentioning

confidence: 99%

“…Understanding the natural history of a disease is fundamental to its management. Since chronic ME is associated with damaging of the ELM, photoreceptor segments, and disorganization of inner retinal layers [12][13][14][15][16][17][18][19][20], the treatment of ME should be performed as soon as possible in order to avoid structural retinal changes and irreversible damage of visual function [20].…”

Section: Introductionmentioning

confidence: 99%

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Effect of Dexamethasone Intravitreal Implant on Visual Acuity and Foveal Photoreceptor Integrity in Macular Edema Secondary to Retinal Vascular Disease

et al. 2020

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Purpose: To evaluate the effect of the dexamethasone intravitreal (DEX) implant on the external limiting membrane (ELM) and ellipsoid zone (EZ) integrity in treatment naïve patients with macular edema (ME) secondary to retinal vascular disease (RVD). Methods: Retrospective study conducted on patients with ME secondary to RVD, who underwent a DEX implant. Results: One-hundred eyes were included. Mean age was 70.3±11.1 years. Mean ELM integrity significantly improved from 1575.9±285.9 μm at baseline to 1711.7±244.0 μm at month 3, p<0.0001. Similarly, there was a significant improvement in EZ integrity from baseline to month-3 (1531.5±317.1 μm versus 1694.3±252.8 μm, respectively, p<0.0001). At month 3, mean visual acuity (VA) gain was 9.9±14.1 letters, p<0.0001. Mean central retinal thickness (CRT) significantly decreased by -193.2±185.7 μm from baseline to month-3, p<0.0001. Mean change in VA and CRT were significantly correlated with baseline ELM integrity (p=0.0065 and p=0.0046, respectively) and EZ (p=0.0300 and p=0.0035, respectively) integrity. At month-3, the proportion of eyes who had an intact ELM (mean difference 16.0%, 95% CI: 5.4% to 26.4%, p=0.0033) and EZ (mean difference 12.0%, 95% CI: 1.8% to 22.1%, p=0.0210) was significantly higher than at baseline. Conclusions: DEX implant was able to significantly improve ELM and EZ integrity in naïve patients with ME.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of Dexamethasone Intravitreal Implant on Visual Acuity and Foveal Photoreceptor Integrity in Macular Edema Secondary to Retinal Vascular Disease

et al. 2020

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“…Since the device is not intended for regular ophthalmic diagnosis, but only for monitoring disease activity, reduced image quality is acceptable as long as morphological changes can be detected with sufficient sensitivity and specificity. Furthermore, mere monitoring of central retinal volume has been shown to be sensitive in AMD monitoring, which could be used as a further monitoring criterion [ 16 ]. In case of disease activity, the device would refer the patient to his doctor for a confirmatory OCT scan and possibly an immediate intravitreal injection in case of confirmed activity.…”

Section: Discussionmentioning

confidence: 99%

Self-examination low-cost full-field OCT (SELFF-OCT) for patients with various macular diseases

Burchard

Moltmann

Tode

et al. 2020

Graefes Arch Clin Exp Ophthalmol

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Purpose The treatment guidelines for many macular diseases rely on frequent monitoring with optical coherence tomography (OCT). However, the burden of frequent disease control leads to low therapy adherence in real life. OCT home monitoring would address this issue but requires an inexpensive and self-operable device. With self-examination low-cost full-field OCT (SELFF-OCT), our group has introduced a novel technology that may fulfill both requirements. In this pilot study, we report the initial experiences with a clinical prototype. Methods Fifty-one patients with different macular diseases were recruited in a cross-sectional study. The most common diseases were age-related macular degeneration (AMD; 39/51), diabetic macular edema (DME; 6/51), and retinal vein occlusion (RVO; 3/51). Patients received a short training in device usage and then performed multiple self-scans with the SELFF-OCT device. For comparison, scans with a standard clinical spectral domain (SD-)OCT were taken. Results After a brief training, 77% of the patients were able to successfully acquire images that were clinically gradable. No significant influence on success could be found for age (p = 0.08) or BCVA (p = 0.97). Relevant disease biomarkers in the most common retinal diseases could be detected. Conclusions SELFF-OCT was used successfully for retinal self-examination and in the future could be used for retinal home monitoring. Future improvements in technology are expected to improve success rates and image quality. Trial registration The Trial was registered in the German Trial Register under the number DRKS00013755 on 14.03.2018.

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“…2 In addition, it has been shown that retinal volume change may also be a reliable biomarker for the activity of AMD. 3 The monitoring frequency plays an important role in the success of AMD treatment. The examination intervals of current treatment schemes are often too large and are not adapted to the patient's individual recurrence pattern.…”

Section: Purposementioning

confidence: 99%

Segmentation of retinal low-cost optical coherence tomography images using deep learning

Kepp

Sudkamp

Burchard

et al. 2020

Medical Imaging 2020: Computer-Aided Diagnosis

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The treatment of age-related macular degeneration (AMD) requires continuous eye exams using optical coherence tomography (OCT). The need for treatment is determined by the presence or change of disease-specific OCTbased biomarkers. Therefore, the monitoring frequency has a significant influence on the success of AMD therapy. However, the monitoring frequency of current treatment schemes is not individually adapted to the patient and therefore often insufficient. While a higher monitoring frequency would have a positive effect on the success of treatment, in practice it can only be achieved with a home monitoring solution. One of the key requirements of a home monitoring OCT system is a computer-aided diagnosis to automatically detect and quantify pathological changes using specific OCT-based biomarkers. In this paper, for the first time, retinal scans of a novel self-examination low-cost full-field OCT (SELF-OCT) are segmented using a deep learningbased approach. A convolutional neural network (CNN) is utilized to segment the total retina as well as pigment epithelial detachments (PED). It is shown that the CNN-based approach can segment the retina with high accuracy, whereas the segmentation of the PED proves to be challenging. In addition, a convolutional denoising autoencoder (CDAE) refines the CNN prediction, which has previously learned retinal shape information. It is shown that the CDAE refinement can correct segmentation errors caused by artifacts in the OCT image.

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Retinal volume change is a reliable OCT biomarker for disease activity in neovascular AMD

Cited by 18 publications

References 17 publications

Effect of Dexamethasone Intravitreal Implant on Visual Acuity and Foveal Photoreceptor Integrity in Macular Edema Secondary to Retinal Vascular Disease

Effect of Dexamethasone Intravitreal Implant on Visual Acuity and Foveal Photoreceptor Integrity in Macular Edema Secondary to Retinal Vascular Disease

Self-examination low-cost full-field OCT (SELFF-OCT) for patients with various macular diseases

Segmentation of retinal low-cost optical coherence tomography images using deep learning

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