OBJECTIVETo investigate the effect of macular fluid volumes (subretinal fluid SRF, intraretinal fluid IRF, and pigment epithelium detachment PED) after initial treatment on functional and structural ...outcomes in neovascular age-related macular degeneration in a real-world cohort from Fight Retinal Blindness!METHODSTreatment-naive neovascular age-related macular degeneration patients from Fight Retinal Blindness! (Zürich, Switzerland) were included. Macular fluid on optical coherence tomography was automatically quantified using an approved artificial intelligence algorithm. Follow-up of macular fluid, number of anti-vascular endothelial growth factor treatments, effect of fluid volumes after initial treatment (high, top 25%; low, bottom 75%) on best-corrected visual acuity, and development of macular atrophy and fibrosis was investigated over 48 months.RESULTSA total of 209 eyes (mean age, 78.3 years) were included. Patients with high IRF volumes after initial treatment differed by -2.6 (p = 0.021) and -7.4 letters (p = 0.007) at months 12 and 48, respectively. Eyes with high IRF received significantly more treatments (+1.6 p < 0.001 and +5.3 p = 0.002 at months 12 and 48, respectively). Patients with high SRF or PED had comparable best-corrected visual acuity outcomes but received significantly more treatments for SRF (+2.4 p < 0.001 and +11.4 p < 0.001 at months 12 and 48, respectively) and PED (+1.2 p = 0.001 and +7.8 p < 0.001 at months 12 and 48, respectively).DISCUSSIONPatients with high macular fluid after initial treatment are at risk of losing vision that may not be compensable with higher treatment frequency for IRF. Higher treatment frequency for SRF and PED may result in comparable treatment outcomes. Quantification of macular fluid in all compartments is essential to detect eyes at risk of aggressive disease.
To investigate the impact of large choroidal vessels (LCV) on Choriocapillaris (CC) flow deficit (FD) analyses with swept-source optical coherence tomography angiography (SS-OCTA) Macular 6x6mm ...SS-OCTA scans were obtained from intermediate age-related macular degeneration (iAMD) and healthy eyes. Images were captured and processed according to most common standards and analyzed for percentage of flow-deficits (FD%) within four 1x1mm squares at the corners of each image. Choroidal thickness (CT), iris color and refraction error were considered as potential influential factors for LCV visibility. A linear mixed model and logistic regression models were calculated for statistical evaluation. Sixty-nine iAMD and 49 age-matched healthy eyes were enrolled. LCV were visible in at least one sector in 52% of iAMD and 47% of healthy eyes. Within the iAMD group FD% were significantly lower in areas containing LCV (p = 0.0029). Increasing CT resulted in an odds ratio decrease of LCV (OR: 0.94, p<0.0001). Below a CT value of less than or equal to118mum LCV could be expected with a sensitivity of 86% and a specificity of 85%. LCV can significantly affect CC FD analyses of SS-OCTA images. Their visibility is negatively associated with CT. The impact of LCV should be taken into account when performing CC FD assessments, especially in patients where reduced CT is to be expected and inclusion of affected areas should be considered carefully.
To prospectively investigate retinal vascular changes in patients undergoing epiretinal membrane (ERM) and internal limiting membrane (ILM) peeling using swept source optical coherence tomography ...angiography (SSOCTA).
Consecutive patients were grouped based on ERM severity and followed using SSOCTA up to month 3 after surgical intervention. Superficial and deep foveal avascular zone (s/dFAZ) as well as foveal and parafoveal vessel density (VD) were correlated with ERM severity and visual acuity. Differences between groups were evaluated.
Significant correlations were found between ERM severity and baseline sFAZ, dFAZ and best corrected visual acuity (BCVA), central retinal subfield thickness (CST) and ΔCST (r = -0.52, r = -0.43, r = -0.42, r = 0.58, r = 0.39; all p<0.05). Vascular flow parameters did not correlate with age, peeling size, pseudophakia or CST, but correlated with intraretinal cysts presence. No associations of BCVA with any of the OCTA parameters across time were found. Significant differences between ERM severity groups 1 and 2 were found for sFAZ at baseline (p = 0.005) and at the 3-month follow-up (p = 0.014), and for dFAZ at baseline (p = 0.017). Superficial foveal and parafoveal VD were not significantly different between groups (all p>0.05).
This study clearly shows that ERM severity based on ERM staging has to be taken into account when undertaking studies in patients with idiopathic ERM using SSOCTA. Further, specific changes in the superficial and deep retinal vasculature in eyes undergoing ERM and ILM peeling were found. However, the clinical usefulness and prognostic value for post-surgical treatment BCVA of the SSOCTA-derived variables (sFAZ and dFAZ area, as well as foveal and parafoveal VD) used remains questionable.
Summary
Due to a legislative amendment in Austria to determine breath alcohol (BrAC) instead of blood alcohol (BAC) in connection with traffic offences, many results of blood alcohol calculations ...were simply converted using distinct conversion factors. In Austria, the transformation of BAC to BrAC was carried out by using a factor of 1:2000, which, however, is commonly known to be too low. Noticing the great demand for a calculation method that is not exclusively based on blood alcohol, a formula for calculating breath alcohol based on blood alcohol was published in 1989, but in which the body surface area (BSA) was considered the most important influencing variable. In order to refine this new method, a liquor intake experiment was conducted combined with measurements of total body water (TBW) as an additional variable, using hand to foot bioelectrical impedance assessment (BIA). The test group comprised 37 men and 40 women to evaluate the accuracy of TBW and BSA as an individual parameter for alcohol concentration. The correlation coefficient of BrAC with TBW was constantly higher than with BSA (maximum = 0.921 at 1 h and 45 min after cessation of alcohol intake). These results are valid for both men and women as well as in a gender independent calculation. Hence, for an accurate back calculation of BrAC adjusted values of eliminations rates had to be found. This study describes mean elimination rates of BrAC for both men (0.065 ± 0.011 mg/L h
−1
) and women (0.074 ± 0.017 mg/L h
−1
). As previously shown women displayed a significantly higher elimination rate than men (
p
= 0.006).
To identify disease activity and effects of intravitreal pegcetacoplan treatment on the topographic progression of geographic atrophy (GA) secondary to age-related macular degeneration quantified in ...spectral-domain OCT (SD-OCT) by automated deep learning assessment.
Retrospective analysis of a phase II clinical trial study evaluating pegcetacoplan in GA patients (FILLY, NCT02503332).
SD-OCT scans of 57 eyes with monthly treatment, 46 eyes with every-other-month (EOM) treatment, and 53 eyes with sham injection from baseline and 12-month follow-ups were included, in a total of 312 scans.
Retinal pigment epithelium loss, photoreceptor (PR) integrity, and hyperreflective foci (HRF) were automatically segmented using validated deep learning algorithms. Local progression rate (LPR) was determined from a growth model measuring the local expansion of GA margins between baseline and 1 year. For each individual margin point, the eccentricity to the foveal center, the progression direction, mean PR thickness, and HRF concentration in the junctional zone were computed. Mean LPR in disease activity and treatment effect conditioned on these properties were estimated by spatial generalized additive mixed-effect models.
LPR of GA, PR thickness, and HRF concentration in μm.
A total of 31,527 local GA margin locations were analyzed. LPR was higher for areas with low eccentricity to the fovea, thinner PR layer thickness, or higher HRF concentration in the GA junctional zone. When controlling for topographic and structural risk factors, we report on average a significantly lower LPR by −28.0% (95% confidence interval CI, −42.8 to −9.4; P = 0.0051) and −23.9% (95% CI, −40.2 to −3.0; P = 0.027) for monthly and EOM-treated eyes, respectively, compared with sham.
Assessing GA progression on a topographic level is essential to capture the pathognomonic heterogeneity in individual lesion growth and therapeutic response. Pegcetacoplan-treated eyes showed a significantly slower GA lesion progression rate compared with sham, and an even slower growth rate toward the fovea. This study may help to identify patient cohorts with faster progressing lesions, in which pegcetacoplan treatment would be particularly beneficial. Automated artificial intelligence–based tools will provide reliable guidance for the management of GA in clinical practice.
Purpose
To evaluate change in retinal layers 18 months after femtosecond laser‐assisted cataract surgery (LCS) and manual cataract surgery (MCS) in a representative age‐related cataract population ...using artificial intelligence (AI)‐based automated retinal layer segmentation.
Methods
This was a prospective, randomized and intraindividual‐controlled study including 60 patients at the Medical University of Vienna, Austria. Bilateral same‐day LCS and MCS were performed in a randomized sequence. To provide insight into the development of cystoid macular oedema (CME), retinal layer thickness was measured pre‐operatively and up to 18 months post‐operatively in the central 1 mm, 3 mm and 6 mm.
Results
Fifty‐six patients completed all follow‐up visits. LCS compared to MCS did not impact any of the investigated retinal layers at any follow‐up visit (p > 0.05). For the central 1 mm, a significant increase in total retinal thickness (TRT) was seen after 1 week followed by an elevated plateau thereafter. For the 3 mm and 6 mm, TRT increased only after 3 weeks and 6 weeks and decreased again until 18 months. TRT remained significantly increased compared to pre‐operative thickness (p < 0.001). Visual acuity remained unaffected by the macular thickening and no case of CME was observed. Inner nuclear layer (INL) and outer nuclear layer (ONL) were the main causative layers for the total TRT increase. Photoreceptors (PR) declined 1 week after surgery but regained pre‐operative values 18 months after surgery.
Conclusion
Low‐energy femtosecond laser pre‐treatment did not influence thickness of the retinal layers in any topographic zone compared to manual high fluidic phacoemulsification. TRT did not return to pre‐operative values 18 months after surgery. The causative layers for subclinical development of CME were successfully identified.
To investigate the therapeutic effect of intravitreal pegcetacoplan on the inhibition of photoreceptor (PR) loss and thinning in geographic atrophy (GA) on conventional spectral-domain OCT (SD-OCT) ...imaging by deep learning–based automated PR quantification.
Post hoc analysis of a prospective, multicenter, randomized, sham (SM)-controlled, masked phase II trial investigating the safety and efficacy of pegcetacoplan for the treatment of GA because of age-related macular degeneration.
Study eyes of 246 patients, randomized 1:1:1 to monthly (AM), bimonthly (AEOM), and SM treatment.
We performed fully automated, deep learning–based segmentation of retinal pigment epithelium (RPE) loss and PR thickness on SD-OCT volumes acquired at baseline and months 2, 6, and 12. The difference in the change of PR loss area was compared among the treatment arms. Change in PR thickness adjacent to the GA borders and the entire 20° scanning area was compared between treatment arms.
Square-root transformed PR loss area in μm or mm, PR thickness in μm, and PR loss/RPE loss ratio.
A total of 31 556 B-scans of 644 SD-OCT volumes of 161 study eyes (AM 52, AEOM 54, SM 56) were evaluated from baseline to month 12. Comparison of the mean change in PR loss area revealed statistically significantly less growth in the AM group at months 2, 6, and 12 than in the SM group (–41 μm ± 219 vs. 77 μm ± 126; P = 0.0004; –5 μm ± 221 vs. 156 μm ± 139; P < 0.0001; 106 μm ± 400 vs. 283 μm ± 226; P = 0.0014). Photoreceptor thinning was significantly reduced under AM treatment compared with SM within the GA junctional zone, as well as throughout the 20° area. A trend toward greater inhibition of PR loss than RPE loss was observed under therapy.
Distinct and reliable quantification of PR loss using deep learning–based algorithms offers an essential tool to evaluate therapeutic efficacy in slowing disease progression. Photoreceptor loss and thinning are reduced by intravitreal complement C3 inhibition. Automated quantification of PR loss/maintenance based on OCT images is an ideal approach to reliably monitor disease activity and therapeutic efficacy in GA management in clinical routine and regulatory trials.
Purpose
To predict visual outcomes and treatment needs in a treat & extend (T&E) regimen in neovascular age-related macular degeneration (nAMD) using a machine learning model based on quantitative ...optical coherence tomography (OCT) imaging biomarkers.
Materials and methods
Study eyes of 270 treatment-naïve subjects, randomized to receiving ranibizumab therapy in the T&E arm of a randomized clinical trial were considered. OCT volume scans were processed at baseline and at the first follow-up visit 4 weeks later. Automated image segmentation was performed, where intraretinal (IRF), subretinal (SRF) fluid, pigment epithelial detachment (PED), hyperreflective foci, and the photoreceptor layer were delineated using a convolutional neural network (CNN). A set of respective quantitative imaging biomarkers were computed across an Early Treatment Diabetic Retinopathy Study (ETDRS) grid to describe the retinal pathomorphology spatially and its change after the first injection. Lastly, using the computed set of OCT features and available clinical and demographic information, predictive models of outcomes and retreatment intervals were built using machine learning and their performance evaluated with a 10-fold cross-validation.
Results
Data of 228 evaluable patients were included, as some had missing scans or were lost to follow-up. Of those patients, 55% reached and maintained long (8, 10, 12 weeks) and another 45% stayed at short (4, 6 weeks) treatment intervals. This provides further evidence for a high disease activity in a major proportion of patients. The model predicted the extendable treatment interval group with an AUROC of 0.71, and the visual outcome with an AUROC of up to 0.87 when utilizing both, clinical and imaging features. The volume of SRF and the volume of IRF, remaining at the first follow-up visit, were found to be the most important predictive markers for treatment intervals and visual outcomes, respectively, supporting the important role of quantitative fluid parameters on OCT.
Conclusion
The proposed Artificial intelligence (AI) methodology was able to predict visual outcomes and retreatment intervals of a T&E regimen from a single injection. The result of this study is an urgently needed step toward AI-supported management of patients with active and progressive nAMD.
To identify the individual progression of geographic atrophy (GA) lesions from baseline OCT images of patients in routine clinical care.
Clinical evaluation of a deep learning-based algorithm.
One ...hundred eighty-four eyes of 100 consecutively enrolled patients.
OCT and fundus autofluorescence (FAF) images (both Spectralis, Heidelberg Engineering) of patients with GA secondary to age-related macular degeneration in routine clinical care were used for model validation. Fundus autofluorescence images were annotated manually by delineating the GA area by certified readers of the Vienna Reading Center. The annotated FAF images were anatomically registered in an automated manner to the corresponding OCT scans, resulting in 2-dimensional en face OCT annotations, which were taken as a reference for the model performance. A deep learning-based method for modeling the GA lesion growth over time from a single baseline OCT was evaluated. In addition, the ability of the algorithm to identify fast progressors for the top 10%, 15%, and 20% of GA growth rates was analyzed.
Dice similarity coefficient (DSC) and mean absolute error (MAE) between manual and predicted GA growth.
The deep learning-based tool was able to reliably identify disease activity in GA using a standard OCT image taken at a single baseline time point. The mean DSC for the total GA region increased for the first 2 years of prediction (0.80–0.82). With increasing time intervals beyond 3 years, the DSC decreased slightly to a mean of 0.70. The MAE was low over the first year and with advancing time slowly increased, with mean values ranging from 0.25 mm to 0.69 mm for the total GA region prediction. The model achieved an area under the curve of 0.81, 0.79, and 0.77 for the identification of the top 10%, 15%, and 20% growth rates, respectively.
The proposed algorithm is capable of fully automated GA lesion growth prediction from a single baseline OCT in a time-continuous fashion in the form of en face maps. The results are a promising step toward clinical decision support tools for therapeutic dosing and guidance of patient management because the first treatment for GA has recently become available.
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant ...attention. The vast amount of high-resolution image data, such as optical coherence tomography (OCT) images, has been a driving force behind AI growth in this field. Age-related macular degeneration (AMD) is one of the leading causes for blindness in the world, affecting approximately 196 million people worldwide in 2020. Multimodal imaging has been for a long time the gold standard for diagnosing patients with AMD, however, currently treatment and follow-up in routine disease management are mainly driven by OCT imaging. AI-based algorithms have by their precision, reproducibility and speed, the potential to reliably quantify biomarkers, predict disease progression and assist treatment decisions in clinical routine as well as academic studies. This review paper aims to provide a summary of the current state of AI in AMD, focusing on its applications, challenges, and prospects.
PDF
