Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning

Correlation of retinal fluid and photoreceptor and RPE loss in neovascular AMD by automated quantification, a real-world FRB! analysis

PURPOSE

To quantify ellipsoid zone (EZ) loss during anti-VEGF therapy for neovascular age-related macular degeneration (nAMD) and correlate these findings with nAMD disease activity using artificial intelligence-based algorithms.

METHODS

Spectral domain optical coherence tomography (Spectralis, Heidelberg Engineering) images from nAMD treatment-naïve patients from the Fight Retinal Blindness! (FRB!) Registry from Zürich, Switzerland were processed at baseline and over 3 years of follow-up. An approved deep learning algorithm (Fluid Monitor, RetInSight) was used to automatically quantify intraretinal fluid (IRF), subretinal fluid (SRF) and pigment epithelial detachment (PED). An ensemble U-net deep learning algorithm was used to automated quantify EZ integrity based on EZ layer thickness. The impact of fluid volumes on EZ thickness and late-stages outcomes were calculated using Wilcoxon rank-sum tests, a linear mixed model and a longitudinal panel regression model.

RESULTS

Two hundred and eleven eyes from 158 patients were included. The mean ± SD EZ loss area in the central 6 mm was 1.81 ± 2.68 mm2 at baseline and reached 6.21 ± 6.15 mm2 at month 36. Higher fluid volumes (top 25%) of IRF and PED in the central 1 and 6 mm of the macula were significantly associated with more advanced EZ thinning and loss compared to the low fluid volume subgroup. The high SRF subgroup in the linear regression model showed no statistically significant association with EZ integrity in the central macula; however, the longitudinal analysis revealed an increased EZ thickness with no additional loss.

CONCLUSIONS

Intraretinal fluid and PED volumes and their resolution pattern have an impact on alteration of the underlying EZ layer. AI-supported quantifications are helpful in quantifying early signs of macular atrophy and providing individual risk profiles as a basis for tailored therapies for optimized visual outcomes.

Artificial intelligence in managing retinal disease-current concepts and relevant aspects for health care providers

Given how the diagnosis and management of many ocular and, most specifically, retinal diseases heavily rely on various imaging modalities, the introduction of artificial intelligence (AI) into this field has been a logical, inevitable, and successful development in recent decades. The field of retinal diseases has practically become a showcase for the use of AI in medicine. In this article, after providing a short overview of the most relevant retinal diseases and their socioeconomic impact, we highlight various aspects of how AI can be applied in research, diagnosis, and disease management and how this is expected to alter patient flows, affecting also health care professionals beyond ophthalmologists.

Quantifications of Outer Retinal Bands in Geographic Atrophy by Comparing Superior Axial Resolution and Conventional OCT

Purpose

Novel treatments for geographic atrophy (GA) require precise monitoring, which can be improved with advances in optical coherence tomography (OCT) technology. The purpose of this study was to investigate the benefits of a novel device with superior axial resolution.

Methods

Patients were recruited at the Department of Ophthalmology and Optometry at the Medical University of Vienna. Patients with GA were imaged with a Heidelberg SPECTRALIS HRA+OCT and the novel Heidelberg High-Res OCT device. Outer retinal bands and subretinal drusenoid deposits (SDDs) were segmented in 49 B-scans per OCT. Thickness and loss of outer retinal bands, as well as SDD volumes, were compared between devices and regions using linear mixed-effects models.

Results

The study included 3920 B-scans of 40 eyes of 32 patients. For the High-Res OCT, the myoid zone was thinner (19.85 µm, 95% confidence interval [CI] 16.8-22.8 vs. 21.37 µm, 95% CI 18.4-24.4; P < 0.001), whereas the ellipsoid zone (EZ) band was thicker (28.35 µm; 95% CI 22.7-24.0 vs. 27.29 µm, 95% CI 21.6-33.0). Smaller EZ- and external limiting membrane loss areas (all P < 0.001) were found for the High-Res OCT. The RPE band was thinner for the High-Res OCT (15.97 µm, 95% CI 13.5-18.4 vs. 21.08 µm, 95% CI 18.6-23.5; P < 0.001) without significant differences in RPE loss. Higher SDD volumes were found for the High-Res OCT (P < 0.001).

Conclusions

Precise in vivo quantification of OCT features is of great relevance for individualized patient management. The High-Res OCT device allows for detailed topographical analysis of outer retinal changes in GA, which could improve early detection, patient selection, and patient management in clinical practice.

Correlation of point-wise retinal sensitivity with localized features of diabetic macular edema using deep learning

OBJECTIVE

To evaluate the association between localized features of diabetic macular edema (DME) and point-wise retinal sensitivity (RS) assessed with microperimetry (MP) using deep learning (DL)-based automated quantification on optical coherence tomography (OCT) scans.

DESIGN

Cross-sectional study.

PARTICIPANTS

Twenty eyes of 20 subjects with clinically significant DME were included in this study.

METHODS

Patients with DME visible on OCT scans (Spectralis Heidelberg Retina Angiograph [HRA]+OCT) completed 2 MP examinations using a custom 45 stimuli grid on MAIA (CenterVue). MP stimuli were coregistered with the corresponding OCT location using image registration algorithms. DL-based algorithms were used to quantify intraretinal fluid (IRF) and ellipsoid zone (EZ) thickness. Hard exudates (HEs) were quantified semiautomatically. Multivariable mixed-effect models were calculated to investigate the association between DME-specific OCT features and point-wise RS. As EZ thickness values below HEs were excluded, the models included either EZ thickness or HEs.

RESULTS

A total of 1800 MP stimuli from 20 eyes of 20 patients were analyzed. Stimuli with IRF (n = 568) showed significantly decreased RS compared to areas without (estimate [95% CI]: -1.11 dB [-1.69, -0.52]; p = 0.0002). IRF volume was significantly negatively (-0.45 dB/nL [-0.71; -0.18]; p = 0.001) and EZ thickness positively (0.14 dB/µm [0.1; 0.19]; p < 0.0001) associated with localized point-wise RS. In the multivariable mixed model, including HE volume instead of EZ thickness, a negative impact on RS was observed (-0.43/0.1 nL [-0.81; -0.05]; p = 0.027).

CONCLUSIONS

DME-specific features, as analyzed on OCT, have a significant impact on point-wise RS. IRF and HE volume showed a negative and EZ thickness, a positive association with localized RS.

Repeatability of Microperimetry in Areas of Retinal Pigment Epithelium and Photoreceptor Loss in Geographic Atrophy Supported by Artificial Intelligence-Based Optical Coherence Tomography Biomarker Quantification

PURPOSE

Growing interest in microperimetry (MP) or fundus-controlled perimetry as a targeted psychometric testing method in geographic atrophy (GA) is warranted because of the disease subclinical/extrafoveal appearance or preexisting foveal loss with visual acuity becoming unreliable. We provide comprehensive pointwise test-retest repeatability reference values on the most widely used MP devices and combine them with targeted testing in areas of retinal pigment epithelium (RPE) as well as photoreceptor (PR) integrity loss, guiding the interpretation of sensitivity loss during the long-term follow-up of patients with GA.

DESIGN

Prospective reliability study.

METHODS

Patients with GA underwent consecutive testing on CenterVue (iCare) MAIA and NIDEK MP3 devices. Obtained pointwise sensitivity (PWS) measurements were spatially coregistered to an optical coherence tomography volume scan acquired during the same visit. Areas with RPE and PR integrity loss, drusen, and PR thickness as well as the volume of hyperreflective foci where identified and quantified using a set of validated deep learning-based algorithms. Test-retest repeatability was assessed according to areas defined by biomarker-specific morphologic changes using Bland-Altmann coefficients of repeatability. Furthermore, the interdevice correlation, the repeatability of scotoma point detection, and any potential effects on fixation stability were assessed.

RESULTS

Nine hundred stimuli per device from 20 subjects were included. Identical overall PWS test-retest variance could be detected for MAIA (±6.57) and MP3 (±6.59). PR integrity loss was associated with a higher test-retest variance on both devices (MAIA, P = .002; MP3, P < .001). Higher coefficients of repeatability for stimuli in areas presenting RPE loss (±10.99 vs ±5.34) or hyperreflective foci (±9.21 vs ±6.25) could only be detected on MP3 examinations (P < .001 and P = .01, respectively). An excellent intradevice correlation (MAIA 0.94 [0.93-0.95], MP3 0.94 [0.94-0.95]) and a good mean interdevice correlation (0.84 [0.53-0.92]) were demonstrated. The chosen device, run order, or absence of foveal sparing had no significant effect on fixation stability.

CONCLUSION

Areas presenting automatically quantified PR integrity loss with and without underlying RPE loss are associated with higher test-retest variance for both MAIA and MP3. These findings are crucial for an accurate interpretation of GA progression during long-term follow-up and the planning of future trials with MP testing as functional study endpoint.

Rethinking Clinical Trials in Age-Related Macular Degeneration: How AI-Based OCT Analysis Can Support Successful Outcomes

Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. Due to an aging population, its prevalence is expected to increase, making novel and optimized therapy options imperative. However, both late-stage forms of the disease, neovascular AMD (nAMD) and geographic atrophy (GA), exhibit considerable variability in disease progression and treatment response, complicating the evaluation of therapeutic efficacy and making it difficult to design clinical trials that are both inclusive and statistically robust. Traditional trial designs frequently rely on generalized endpoints that may not fully capture the nuanced benefits of treatment, particularly in diseases like GA, where functional improvements can be gradual or subtle. Artificial intelligence (AI) has the potential to address these issues by identifying novel, condition-specific biomarkers or endpoints, enabling precise patient stratification and improving recruitment strategies. By providing an overview of the advances and application of AI-based optical coherence tomography analysis in the context of AMD clinical trials, this review highlights the transformative potential of AI in optimizing clinical trial outcomes for patients with nAMD or GA secondary to AMD.

Photoreceptor assessment in age-related macular degeneration

Clinical trials investigating drugs for various stages of age-related macular degeneration (AMD) are actively underway and there is a strong interest in outcomes that demonstrate a structure-function-correlation. The ellipsoid zone (EZ), a crucial anatomical feature affected in this disease, has emerged as a strong contender. There is significant interest in evaluating EZ metrics on Optical Coherence Tomography (OCT), such as integrity and reflectivity, as disruption of this photoreceptor-rich layer may indicate disease progression. Loss of photoreceptor integrity in the junctional zone of geographic atrophy (GA) has been shown to exceed the areas of retinal pigment epithelial (RPE) atrophy, thus predicting future GA expansion. Furthermore, reduced visual acuity and retinal sensitivity have been correlated with loss of EZ integrity, underscoring a structure-function relationship. Photoreceptor integrity has also recently been acknowledged by the Food and Drug Administration (FDA), supporting its use as a primary endpoint in clinical trials investigating treatments for GA. However, the segmentation of this EZ still poses challenges. Continuous enhancements in OCT resolution and advancements in automated segmentation algorithms contribute to improved assessment of the EZ, strengthening its potential as an imaging biomarker for assessing photoreceptor function. It remains to be seen whether the EZ will serve as a surrogate marker for intermediate AMD. This article aims to provide an overview of the current understanding and knowledge of the EZ, while addressing ongoing challenges encountered in its assessment and interpretation.

Artificial intelligence in assessing progression of age-related macular degeneration

The human population is steadily growing with increased life expectancy, impacting the prevalence of age-dependent diseases, including age-related macular degeneration (AMD). Health care systems are confronted with an increasing burden with rising patient numbers accompanied by ongoing developments of therapeutic approaches. Concurrent advances in imaging modalities provide eye care professionals with a large amount of data for each patient. Furthermore, with continuous progress in therapeutics, there is an unmet need for reliable structural and functional biomarkers in clinical trials and practice to optimize personalized patient care and evaluate individual responses to treatment. A fast and objective solution is Artificial intelligence (AI), which has revolutionized assessment of AMD in all disease stages. Reliable and validated AI-algorithms can aid to overcome the growing number of patients, visits and necessary treatments as well as maximize the benefits of multimodal imaging in clinical trials. Therefore, there are ongoing efforts to develop and validate automated algorithms to unlock more information from datasets allowing automated assessment of disease activity and disease progression. This review aims to present selected AI algorithms, their development, applications and challenges regarding assessment and prediction of AMD progression.

Disease Activity and Therapeutic Response to Pegcetacoplan for Geographic Atrophy Identified by Deep Learning-Based Analysis of OCT

PURPOSE

To quantify morphological changes of the photoreceptors (PRs) and retinal pigment epithelium (RPE) layers under pegcetacoplan therapy in geographic atrophy (GA) using deep learning-based analysis of OCT images.

DESIGN

Post hoc longitudinal image analysis.

PARTICIPANTS

Patients with GA due to age-related macular degeneration from 2 prospective randomized phase III clinical trials (OAKS and DERBY).

METHODS

Deep learning-based segmentation of RPE loss and PR degeneration, defined as loss of the ellipsoid zone (EZ) layer on OCT, over 24 months.

MAIN OUTCOME MEASURES

Change in the mean area of RPE loss and EZ loss over time in the pooled sham arms and the pegcetacoplan monthly (PM)/pegcetacoplan every other month (PEOM) treatment arms.

RESULTS

A total of 897 eyes of 897 patients were included. There was a therapeutic reduction of RPE loss growth by 22% and 20% in OAKS and 27% and 21% in DERBY for PM and PEOM compared with sham, respectively, at 24 months. The reduction on the EZ level was significantly higher with 53% and 46% in OAKS and 47% and 46% in DERBY for PM and PEOM compared with sham at 24 months. The baseline EZ-RPE difference had an impact on disease activity and therapeutic response. The therapeutic benefit for RPE loss increased with larger EZ-RPE difference quartiles from 21.9%, 23.1%, and 23.9% to 33.6% for PM versus sham (all P < 0.01) and from 13.6% (P = 0.11), 23.8%, and 23.8% to 20.0% for PEOM versus sham (P < 0.01) in quartiles 1, 2, 3, and 4, respectively, at 24 months. The therapeutic reduction of EZ loss increased from 14.8% (P = 0.09), 33.3%, and 46.6% to 77.8% (P < 0.0001) between PM and sham and from 15.9% (P = 0.08), 33.8%, and 52.0% to 64.9% (P < 0.0001) between PEOM and sham for quartiles 1 to 4 at 24 months.

CONCLUSIONS

Deep learning-based OCT analysis objectively identifies and quantifies PR and RPE degeneration in GA. Reductions in further EZ loss on OCT are even higher than the effect on RPE loss in phase 3 trials of pegcetacoplan treatment. The EZ-RPE difference has a strong impact on disease progression and therapeutic response. Identification of patients with higher EZ-RPE loss difference may become an important criterion for the management of GA secondary to AMD.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Topographic and quantitative correlation of structure and function using deep learning in subclinical biomarkers of intermediate age-related macular degeneration

To examine the morphological impact of deep learning (DL)-quantified biomarkers on point-wise sensitivity (PWS) using microperimetry (MP) and optical coherence tomography (OCT) in intermediate AMD (iAMD). Patients with iAMD were examined by OCT (Spectralis). DL-based algorithms quantified ellipsoid zone (EZ)-thickness, hyperreflective foci (HRF) and drusen volume. Outer nuclear layer (ONL)-thickness and subretinal drusenoid deposits (SDD) were quantified by human experts. All patients completed four MP examinations using an identical custom 45 stimuli grid on MP-3 (NIDEK) and MAIA (CenterVue). MP stimuli were co-registered with corresponding OCT using image registration algorithms. Multivariable mixed-effect models were calculated. 3.600 PWS from 20 eyes of 20 patients were analyzed. Decreased EZ thickness, decreased ONL thickness, increased HRF and increased drusen volume had a significant negative effect on PWS (all p < 0.001) with significant interaction with eccentricity (p < 0.001). Mean PWS was 26.25 ± 3.43 dB on MP3 and 22.63 ± 3.69 dB on MAIA. Univariate analyses revealed a negative association of PWS and SDD (p < 0.001). Subclinical changes in EZ integrity, HRF and drusen volume are quantifiable structural biomarkers associated with reduced retinal function. Topographic co-registration between structure on OCT volumes and sensitivity in MP broadens the understanding of pathognomonic biomarkers with potential for evaluation of quantifiable functional endpoints.

Artificial intelligence for geographic atrophy: pearls and pitfalls

PURPOSE OF REVIEW

This review aims to address the recent advances of artificial intelligence (AI) in the context of clinical management of geographic atrophy (GA), a vision-impairing late-stage manifestation of age-related macular degeneration (AMD).

RECENT FINDINGS

Recent literature shows substantial advancements in the development of AI systems to segment GA lesions on multimodal retinal images, including color fundus photography (CFP), fundus autofluorescence (FAF) and optical coherence tomography (OCT), providing innovative solutions to screening and early diagnosis. Especially, the high resolution and 3D-nature of OCT has provided an optimal source of data for the training and validation of novel algorithms. The use of AI to measure progression in the context of newly approved GA therapies, has shown that AI methods may soon be indispensable for patient management. To date, while many AI models have been reported on, their implementation in the real-world has only just started. The aim is to make the benefits of AI-based personalized treatment accessible and far-reaching.

SUMMARY

The most recent advances (pearls) and challenges (pitfalls) associated with AI methods and their clinical implementation in the context of GA will be discussed.

AI in the clinical management of GA: A novel therapeutic universe requires novel tools

Regulatory approval of the first two therapeutic substances for the management of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) is a major breakthrough following failure of numerous previous trials. However, in the absence of therapeutic standards, diagnostic tools are a key challenge as functional parameters in GA are hard to provide. The majority of anatomical biomarkers are subclinical, necessitating advanced and sensitive image analyses. In contrast to fundus autofluorescence (FAF), optical coherence tomography (OCT) provides high-resolution visualization of neurosensory layers, including photoreceptors, and other features that are beyond the scope of human expert assessment. Artificial intelligence (AI)-based methodology strongly enhances identification and quantification of clinically relevant GA-related sub-phenotypes. Introduction of OCT-based biomarker analysis provides novel insight into the pathomechanisms of disease progression and therapeutic, moving beyond the limitations of conventional descriptive assessment. Accordingly, the Food and Drug Administration (FDA) has provided a paradigm-shift in recognizing ellipsoid zone (EZ) attenuation as a primary outcome measure in GA clinical trials. In this review, the transition from previous to future GA classification and management is described. With the advent of AI tools, diagnostic and therapeutic concepts have changed substantially in monitoring and screening of GA disease. Novel technology combined with pathophysiological knowledge and understanding of the therapeutic response to GA treatments, is currently opening the path for an automated, efficient and individualized patient care with great potential to improve access to timely treatment and reduce health disparities.

Influence of OCT biomarkers on microperimetry intra- and interdevice repeatability in diabetic macular edema

To evaluate the intra- and interdevice repeatability of microperimetry (MP) assessments in patients with diabetic macular edema (DME) two consecutive MP testings (45 fovea-centered stimuli, 4-2 staircase strategy) were performed using MP3 (NIDEK, Aichi, Japan) and MAIA (CenterVue, Padova, Italy), respectively. Intraretinal fluid (IRF) and ellipsoid zone (EZ) thickness were automatically segmented by published deep learning algorithms. Hard exudates (HEs) were annotated semi-automatically and disorganization of retinal inner layers (DRIL) was segmented manually. Point-to-point registration of MP stimuli to corresponding spectral-domain OCT (Spectralis, Heidelberg Engineering, Germany) locations was performed for both devices. Repeatability was assessed overall and in areas of disease-specific OCT biomarkers using Bland-Altmann coefficients of repeatability (CoR). A total of 3600 microperimetry stimuli were tested in 20 eyes with DME. Global CoR was high using both devices (MP3: ± 6.55 dB, MAIA: ± 7.69 dB). Higher retest variances were observed in stimuli with IRF (MP3: CoR ± 7.4 dB vs. ± 6.0 dB, p = 0.001, MAIA: CoR ± 9.2dB vs. ± 6.8 dB, p = 0.002) and DRIL on MP3 (CoR ± 6.9 dB vs. ± 3.2 dB, p < 0.001) compared to stimuli without. Repeatabilities were reduced in areas with thinner EZ layers (both p < 0.05). Fixation (Fuji classification) was relatively unstable independent of device and run. These findings emphasize taking higher caution using MP in patients with DME.

Quantitative comparison of automated OCT and conventional FAF-based geographic atrophy measurements in the phase 3 OAKS/DERBY trials

With the approval of the first two substances for the treatment of geographic atrophy (GA) secondary to age-related macular degeneration (AMD), a standardized monitoring of patients treated with complement inhibitors in clinical practice is needed. Optical coherence tomography (OCT) provides high-resolution access to the retinal pigment epithelium (RPE) and neurosensory layers, such as the ellipsoid zone (EZ), which further enhances the understanding of disease progression and therapeutic effects in GA compared to conventional fundus autofluorescence (FAF). In addition, artificial intelligence-based methodology allows the identification and quantification of GA-related pathology on OCT in an objective and standardized manner. The purpose of this study was to comprehensively evaluate automated OCT monitoring for GA compared to reading center-based manual FAF measurements in the largest successful phase 3 clinical trial data of complement inhibitor therapy to date. Automated OCT analysis of RPE loss showed a high and consistent correlation to manual GA measurements on conventional FAF. EZ loss on OCT was generally larger than areas of RPE loss, supporting the hypothesis that EZ loss exceeds underlying RPE loss as a fundamental pathophysiology in GA progression. Automated OCT analysis is well suited to monitor disease progression in GA patients treated in clinical practice and clinical trials.

Long-term impact of low-energy femtosecond laser and manual cataract surgery on macular layer thickness: A prospective randomized study

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.

ADVANCES IN PHOTORECEPTOR AND RETINAL PIGMENT EPITHELIUM QUANTIFICATIONS IN INTERMEDIATE AGE-RELATED MACULAR DEGENERATION: High-Res Versus Standard SPECTRALIS Optical Coherence Tomography

PURPOSE

In this study, differences in retinal feature visualization of high-resolution optical coherence tomography (OCT) devices were investigated with different axial resolutions in quantifications of retinal pigment epithelium and photoreceptors (PRs) in intermediate age-related macular degeneration.

METHODS

Patients were imaged with standard SPECTRALIS HRA + OCT and the investigational High-Res OCT device (both by Heidelberg Engineering, Heidelberg, Germany). Drusen, retinal pigment epithelium, and PR layers were segmented using validated artificial intelligence-based algorithms followed by manual corrections. Thickness and drusen maps were computed for all patients. Loss and thickness measurements were compared between devices, drusen versus nondrusen areas, and early treatment diabetic retinopathy study subfields using mixed-effects models.

RESULTS

Thirty-three eyes from 28 patients with intermediate age-related macular degeneration were included. Normalized PR integrity loss was significantly higher with 4.6% for standard OCT compared with 2.5% for High-Res OCT. The central and parafoveal PR integrity loss was larger than the perifoveal loss (P < 0.05). Photoreceptor thickness was increased on High-Res OCT and in nondrusen regions (P < 0.001). Retinal pigment epithelium appeared thicker on standard OCT and above drusen (P < 0.01).

CONCLUSION

Our study shows that High-Res OCT is able to identify the condition of investigated layers in intermediate age-related macular degeneration with higher precision. This improved in vivo imaging technology might promote our understanding of the pathophysiology and progression of age-related macular degeneration.

Sequence of Morphological Changes Preceding Atrophy in Intermediate AMD Using Deep Learning

Purpose

Investigating the sequence of morphological changes preceding outer plexiform layer (OPL) subsidence, a marker preceding geographic atrophy, in intermediate AMD (iAMD) using high-precision artificial intelligence (AI) quantifications on optical coherence tomography imaging.

Methods

In this longitudinal observational study, individuals with bilateral iAMD participating in a multicenter clinical trial were screened for OPL subsidence and RPE and outer retinal atrophy. OPL subsidence was segmented on an A-scan basis in optical coherence tomography volumes, obtained 6-monthly with 36 months follow-up. AI-based quantification of photoreceptor (PR) and outer nuclear layer (ONL) thickness, drusen height and choroidal hypertransmission (HT) was performed. Changes were compared between topographic areas of OPL subsidence (AS), drusen (AD), and reference (AR).

Results

Of 280 eyes of 140 individuals, OPL subsidence occurred in 53 eyes from 43 individuals. Thirty-six eyes developed RPE and outer retinal atrophy subsequently. In the cohort of 53 eyes showing OPL subsidence, PR and ONL thicknesses were significantly decreased in AS compared with AD and AR 12 and 18 months before OPL subsidence occurred, respectively (PR: 20 µm vs. 23 µm and 27 µm [P < 0.009]; ONL, 84 µm vs. 94 µm and 98 µm [P < 0.008]). Accelerated thinning of PR (0.6 µm/month; P < 0.001) and ONL (0.8 µm/month; P < 0.001) was observed in AS compared with AD and AR. Concomitant drusen regression and hypertransmission increase at the occurrence of OPL subsidence underline the atrophic progress in areas affected by OPL subsidence.

Conclusions

PR and ONL thinning are early subclinical features associated with subsequent OPL subsidence, an indicator of progression toward geographic atrophy. AI algorithms are able to predict and quantify morphological precursors of iAMD conversion and allow personalized risk stratification.

Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema

BACKGROUND

Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges.

MAIN TEXT

This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications.

CONCLUSION

AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.

[Complement inhibition treatment for geographic atrophy (GA): functional and morphological efficacy and relevant biomarkers in clinical practice]

The approval of complement inhibitory therapeutic agents for the treatment of geographic atrophy (GA) has highlighted the need for reliable and reproducible measurement of disease progression and therapeutic efficacy. Due to its availability and imaging characteristics optical coherence tomography (OCT) is the method of choice. Using OCT analysis based on artificial intelligence (AI), the therapeutic efficacy of pegcetacoplan was demonstrated at the levels of both the retinal pigment epithelium (RPE) and photoreceptors (PR). Cloud-based solutions that enable monitoring of GA are already available.

AI-based support for optical coherence tomography in age-related macular degeneration

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.

Analysing early changes of photoreceptor layer thickness following surgery in eyes with epiretinal membranes

BACKGROUND/OBJECTIVES

To analyse short-term changes of mean photoreceptor thickness (PRT) on the ETDRS-grid after vitrectomy and membrane peeling in patients with epiretinal membrane (ERM).

SUBJECTS/METHODS

Forty-eight patients with idiopathic ERM were included in this prospective study. Study examinations comprised best-corrected visual acuity (BCVA) and optical coherence tomography (OCT) before surgery, 1 week (W1), 1 month (M1) and 3 months (M3) after surgery. Mean PRT was assessed using an automated algorithm and correlated with BCVA and central retinal thickness (CRT).

RESULTS

Regarding PRT changes of the study eye in comparison to baseline values, a significant decrease at W1 in the 1 mm, 3 mm and 6 mm area (all p-values < 0.001), at M1 (p = 0.009) and M3 (p = 0.019) in the central 1 mm area, a significant increase at M3 in the 6 mm area (p < 0.001), but no significant change at M1 in the 3 mm and 6 mm area and M3 in the 3 mm area (all p-values > 0.05) were observed. BCVA increased significantly from baseline to M3 (0.3LogMAR-0.15LogMAR, Snellen equivalent = 20/40-20/28 respectively; p < 0.001). There was no correlation between baseline PRT and BCVA at any visit after surgery, nor between PRT and BCVA at any visit (all p-values > 0.05). Decrease in PRT in the 1 mm (p < 0.001), 3 mm (p = 0.013) and 6 mm (p = 0.034) area after one week correlated with the increase in CRT (449.9 µm-462.2 µm).

CONCLUSIONS

Although the photoreceptor layer is morphologically affected by ERMs and after their surgical removal, it is not correlated to BCVA. Thus, patients with photoreceptor layer alterations due to ERM may still benefit from surgery and achieve good functional rehabilitation thereafter.

Deep-learning-based automated measurement of outer retinal layer thickness for use in the assessment of age-related macular degeneration, applicable to both swept-source and spectral-domain OCT imaging

Effective biomarkers are required for assessing the progression of age-related macular degeneration (AMD), a prevalent and progressive eye disease. This paper presents a deep learning-based automated algorithm, applicable to both swept-source OCT (SS-OCT) and spectral-domain OCT (SD-OCT) scans, for measuring outer retinal layer (ORL) thickness as a surrogate biomarker for outer retinal degeneration, e.g., photoreceptor disruption, to assess AMD progression. The algorithm was developed based on a modified TransUNet model with clinically annotated retinal features manifested in the progression of AMD. The algorithm demonstrates a high accuracy with an intersection of union (IoU) of 0.9698 in the testing dataset for segmenting ORL using both SS-OCT and SD-OCT datasets. The robustness and applicability of the algorithm are indicated by strong correlation (r = 0.9551, P < 0.0001 in the central-fovea 3 mm-circle, and r = 0.9442, P < 0.0001 in the 5 mm-circle) and agreement (the mean bias = 0.5440 um in the 3-mm circle, and 1.392 um in the 5-mm circle) of the ORL thickness measurements between SS-OCT and SD-OCT scans. Comparative analysis reveals significant differences (P < 0.0001) in ORL thickness among 80 normal eyes, 30 intermediate AMD eyes with reticular pseudodrusen, 49 intermediate AMD eyes with drusen, and 40 late AMD eyes with geographic atrophy, highlighting its potential as an independent biomarker for predicting AMD progression. The findings provide valuable insights into the ORL alterations associated with different stages of AMD and emphasize the potential of ORL thickness as a sensitive indicator of AMD severity and progression.

Endpoints for clinical trials in ophthalmology

With the identification of novel targets, the number of interventional clinical trials in ophthalmology has increased. Visual acuity has for a long time been considered the gold standard endpoint for clinical trials, but in the recent years it became evident that other endpoints are required for many indications including geographic atrophy and inherited retinal disease. In glaucoma the currently available drugs were approved based on their IOP lowering capacity. Some recent findings do, however, indicate that at the same level of IOP reduction, not all drugs have the same effect on visual field progression. For neuroprotection trials in glaucoma, novel surrogate endpoints are required, which may either include functional or structural parameters or a combination of both. A number of potential surrogate endpoints for ophthalmology clinical trials have been identified, but their validation is complicated and requires solid scientific evidence. In this article we summarize candidates for clinical endpoints in ophthalmology with a focus on retinal disease and glaucoma. Functional and structural biomarkers, as well as quality of life measures are discussed, and their potential to serve as endpoints in pivotal trials is critically evaluated.

[Monitoring of the progression of geographic atrophy with optical coherence tomography]

With the prospect of available therapy for geographic atrophy in the near future and consequently increasing patient numbers, appropriate management strategies for the clinical practice are needed. Optical coherence tomography (OCT) as well as automated OCT analysis using artificial intelligence algorithms provide optimal conditions for assessing disease activity as well as the treatment response for geographic atrophy through a rapid, precise and resource-efficient evaluation.

Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema

Diabetic macular edema (DME) is the primary cause of central vision impairment in patients with diabetes and the leading cause of preventable blindness in working-age people. With the advent of optical coherence tomography and antivascular endothelial growth factor (anti-VEGF) therapy, the diagnosis, evaluation, and treatment of DME were greatly revolutionized in the last decade. However, there is tremendous heterogeneity among DME patients, and 30%-50% of DME patients do not respond well to anti-VEGF agents. In addition, there is no evidence-based and universally accepted administration regimen. The identification of DME patients not responding to anti-VEGF agents and the determination of the optimal administration interval are the 2 major challenges of DME, which are difficult to achieve with the coarse granularity of conventional health care modality. Therefore, more and more retina specialists have pointed out the necessity of introducing precision medicine into the management of DME and have conducted related studies in recent years. One of the most frontier methods is the targeted extraction of individualized disease features from optical coherence tomography images based on artificial intelligence technology, which provides precise evaluation and risk classification of DME. This review aims to provide an overview of the progress of artificial intelligence-enabled precision medicine in automated screening, precise evaluation, prognosis prediction, and follow-up monitoring of DME. Further, the challenges ahead of real-world applications and the future development of precision medicine in DME will be discussed.

Prediction of postoperative visual acuity in patients with age-related cataracts using macular optical coherence tomography-based deep learning method

Background

To predict postoperative visual acuity (VA) in patients with age-related cataracts using macular optical coherence tomography-based deep learning method.

Methods

A total of 2,051 eyes from 2,051 patients with age-related cataracts were included. Preoperative optical coherence tomography (OCT) images and best-corrected visual acuity (BCVA) were collected. Five novel models (I, II, III, IV, and V) were proposed to predict postoperative BCVA. The dataset was randomly divided into a training (n = 1,231), validation (n = 410), and test set (n = 410). The performance of the models in predicting exact postoperative BCVA was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The performance of the models in predicting whether postoperative BCVA was improved by at least two lines in the visual chart (0.2LogMAR) was evaluated using precision, sensitivity, accuracy, F1 and area under curve (AUC).

Results

Model V containing preoperative OCT images with horizontal and vertical B-scans, macular morphological feature indices, and preoperative BCVA had a better performance in predicting postoperative VA, with the lowest MAE (0.1250 and 0.1194LogMAR) and RMSE (0.2284 and 0.2362LogMAR), and the highest precision (90.7% and 91.7%), sensitivity (93.4% and 93.8%), accuracy (88% and 89%), F1 (92% and 92.7%) and AUCs (0.856 and 0.854) in the validation and test datasets, respectively.

Conclusion

The model had a good performance in predicting postoperative VA, when the input information contained preoperative OCT scans, macular morphological feature indices, and preoperative BCVA. The preoperative BCVA and macular OCT indices were of great significance in predicting postoperative VA in patients with age-related cataracts.

Automated Identification and Segmentation of Ellipsoid Zone At-Risk Using Deep Learning on SD-OCT for Predicting Progression in Dry AMD

BACKGROUND

The development and testing of a deep learning (DL)-based approach for detection and measurement of regions of Ellipsoid Zone (EZ) At-Risk to study progression in nonexudative age-related macular degeneration (AMD).

METHODS

Used in DL model training and testing were 341 subjects with nonexudative AMD with or without geographic atrophy (GA). An independent dataset of 120 subjects were used for testing model performance for prediction of GA progression. Accuracy, specificity, sensitivity, and intraclass correlation coefficient (ICC) for DL-based EZ At-Risk percentage area measurement was calculated. Random forest-based feature ranking of EZ At-Risk was compared to previously validated quantitative OCT-based biomarkers.

RESULTS

The model achieved a detection accuracy of 99% (sensitivity = 99%; specificity = 100%) for EZ At-Risk. Automatic EZ At-Risk measurement achieved an accuracy of 90% (sensitivity = 90%; specificity = 84%) and the ICC compared to ground truth was high (0.83). In the independent dataset, higher baseline mean EZ At-Risk correlated with higher progression to GA at year 5 (p < 0.001). EZ At-Risk was a top ranked feature in the random forest assessment for GA prediction.

CONCLUSIONS

This report describes a novel high performance DL-based model for the detection and measurement of EZ At-Risk. This biomarker showed promising results in predicting progression in nonexudative AMD patients.

Deep Learning-Based System for Disease Screening and Pathologic Region Detection From Optical Coherence Tomography Images

Purpose

This study was designed to apply deep learning models in retinal disease screening and lesion detection based on optical coherence tomography (OCT) images.

Methods

We collected 37,138 OCT images from 775 patients and labelled by ophthalmologists. Multiple deep learning models including ResNet50 and YOLOv3 were developed to identify the types and locations of diseases or lesions based on the images.

Results

The model were evaluated using patient-based independent holdout set. For binary classification of OCT images with or without lesions, the performance accuracy was 98.5%, sensitivity was 98.7%, specificity was 98.4%, and the F1 score was 97.7%. For multiclass multilabel disease classification, the models was able to detect vitreomacular traction syndrome and age-related macular degeneration both with an accuracy of more than 99%, sensitivity of more than 98%, specificity of more than 98%, and an F1 score of more than 97%. For lesion location detection, the recalls for different lesion types ranged from 87.0% (epiretinal membrane) to 98.2% (macular pucker).

Conclusions

Deep learning-based models have potentials to aid retinal disease screening, classification and diagnosis with excellent performance, which may serve as useful references for ophthalmologists.

Translational Relevance

The deep learning-based models are capable of identifying and predicting different eye diseases and lesions from OCT images and may have potential clinical application to assist the ophthalmologists for fast and accuracy retinal disease screening.

Predicting Topographic Disease Progression and Treatment Response of Pegcetacoplan in Geographic Atrophy Quantified by Deep Learning

PURPOSE

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.

DESIGN

Retrospective analysis of a phase II clinical trial study evaluating pegcetacoplan in GA patients (FILLY, NCT02503332).

SUBJECTS

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.

METHODS

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.

MAIN OUTCOME MEASURES

LPR of GA, PR thickness, and HRF concentration in μm.

RESULTS

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.

CONCLUSIONS

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.

Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence

Purpose

To predict visual outcomes and treatment needs in a treat &amp; extend (T&amp;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&amp;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&amp;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.

The effect of pegcetacoplan treatment on photoreceptor maintenance in geographic atrophy monitored by AI-based OCT analysis

PURPOSE

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-optical coherence tomography (SD-OCT) imaging by deep learning-based automated PR quantification.

DESIGN

Post-hoc analysis of a prospective, multicenter, randomized, sham-controlled, masked phase II trial investigating the safety and efficacy of pegcetacoplan for the treatment of GA due to age-related macular degeneration.

PARTICIPANTS

Study eyes of 246 patients, randomized 1:1:1 to monthly (AM), bimonthly (AEOM) and sham (SM) treatment.

METHODS

We performed fully automated, deep learning-based segmentation of retinal pigment epithelium (RPE) loss and PR thickness on SD-OCT volumes acquired at baseline, month 2, 6 and 12. The difference in the change of PR loss area was compared between treatment arms. Change in PR thickness adjacent to the GA borders and in the whole 20 degrees scanning area was compared between treatment arms.

MAIN OUTCOME MEASURES

Square root transformed PR loss area in μm or mm, PR thickness in μm, PR loss/RPE loss ratio.

RESULTS

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 mean change in PR loss area revealed statistically significantly less growth in the AM group at month 2, 6 and 12 compared to SM (-41μm ± 219 vs. 77μm ± 126, p=0.0004; -5μm ± 221 vs. 156μm ± 139, p<0.0001; 106μm ± 400 vs. 283μm ± 226 p=0.0014). PR thinning was significantly reduced under monthly treatment compared to sham within the GA junctional zone as well as throughout the 20 degrees area. A trend towards greater inhibition of PR loss compared to RPE loss was observed under therapy.

CONCLUSIONS

Distinct and reliable quantification of PR loss using deep learning-based algorithms offers an essential tool to evaluate therapeutic efficacy in slowing disease progression. PR 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.

Quantitative assessment of retinal fluid in neovascular age-related macular degeneration under anti-VEGF therapy

The retinal world has been revolutionized by optical coherence tomography (OCT) and anti-vascular endothelial growth factor (VEGF) therapy. The numbers of intravitreal injections are on a constant rise and management in neovascular age-related macular degeneration (nAMD) is mainly driven by the qualitative assessment of macular fluid as detected on OCT scans. The presence of macular fluid, particularly subretinal fluid (SRF) and intraretinal fluid (IRF), has been used to trigger re-treatments in clinical trials and the real world. However, large discrepancies can be found between the evaluations of different readers or experts and especially small amounts of macular fluid might be missed during this process. Pixel-wise detection of macular fluid uses an entire OCT volume to calculate exact volumes of retinal fluid. While manual annotations of such pixel-wise fluid detection are unfeasible in a clinical setting, artificial intelligence (AI) is able to overcome this hurdle by providing real-time results of macular fluid in different retinal compartments. Quantitative fluid assessments have been used for various post hoc analyses of randomized controlled trials, providing novel insights into anti-VEGF treatment regimens. Nonetheless, the application of AI-algorithms in a prospective patient care setting is still limited. In this review, we discuss the use of quantitative fluid assessment in nAMD during anti-VEGF therapy and provide an outlook to novel forms of patient care with the support of AI quantifications.

AI-based monitoring of retinal fluid in disease activity and under therapy

Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by high-resolution three-dimensional optical coherence tomography (OCT), which is used world-wide as a diagnostic gold standard largely replacing clinical examination. Artificial intelligence (AI) with its capability to objectively identify, localize and quantify fluid introduces fully automated tools into OCT imaging for personalized disease management. Deep learning performance has already proven superior to human experts, including physicians and certified readers, in terms of accuracy and speed. Reproducible measurement of retinal fluid relies on precise AI-based segmentation methods that assign a label to each OCT voxel denoting its fluid type such as intraretinal fluid (IRF) and subretinal fluid (SRF) or pigment epithelial detachment (PED) and its location within the central 1-, 3- and 6-mm macular area. Such reliable analysis is most relevant to reflect differences in pathophysiological mechanisms and impacts on retinal function, and the dynamics of fluid resolution during therapy with different regimens and substances. Yet, an in-depth understanding of the mode of action of supervised and unsupervised learning, the functionality of a convolutional neural net (CNN) and various network architectures is needed. Greater insight regarding adequate methods for performance, validation assessment, and device- and scanning-pattern-dependent variations is necessary to empower ophthalmologists to become qualified AI users. Fluid/function correlation can lead to a better definition of valid fluid variables relevant for optimal outcomes on an individual and a population level. AI-based fluid analysis opens the way for precision medicine in real-world practice of the leading retinal diseases of modern times.

Spatio-temporal alterations in retinal and choroidal layers in the progression of age-related macular degeneration (AMD) in optical coherence tomography

Age-related macular degeneration (AMD) is the predominant cause of vision loss in the elderly with a major impact on ageing societies and healthcare systems. A major challenge in AMD management is the difficulty to determine the disease stage, the highly variable progression speed and the risk of conversion to advanced AMD, where irreversible functional loss occurs. In this study we developed an optical coherence tomography (OCT) imaging based spatio-temporal reference frame to characterize the morphologic progression of intermediate age-related macular degeneration (AMD) and to identify distinctive patterns of conversion to the advanced stages macular neovascularization (MNV) and macular atrophy (MA). We included 10,040 OCT volumes of 518 eyes with intermediate AMD acquired according to a standardized protocol in monthly intervals over two years. Two independent masked retina specialists determined the time of conversion to MNV or MA. All scans were aligned to a common reference frame by intra-patient and inter-patient registration. Automated segmentations of retinal layers and the choroid were computed and en-face maps were transformed into the common reference frame. Population maps were constructed in the subgroups converting to MNV (n=135), MA (n=50) and in non-progressors (n=333). Topographically resolved maps of changes were computed and tested for statistical significant differences. The development over time was analysed by a joint model accounting for longitudinal and right-censoring aspect. Significantly enhanced thinning of the outer nuclear layer (ONL) and retinal pigment epithelium (RPE)-photoreceptorinner segment/outer segment (PR-IS/OS) layers within the central 3 mm and a faster thinning speed preceding conversion was documented for MA progressors. Converters to MNV presented an accelerated thinning of the choroid and appearance changes in the choroid prior to MNV onset. The large-scale automated image analysis allowed us to distinctly assess the progression of morphologic changes in intermediate AMD based on conventional OCT imaging. Distinct topographic and temporal patterns allow to prospectively determine eyes with risk of progression and thereby greatly improving early detection, prevention and development of novel therapeutic strategies.

Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning

Background

This study aimed to predict the treatment outcomes in patients with diabetic macular edema (DME) after 3 monthly anti-vascular endothelial growth factor (VEGF) injections using machine learning (ML) based on pretreatment optical coherence tomography (OCT) images and clinical variables.

Methods

An ensemble ML system consisting of four deep learning (DL) models and five classical machine learning (CML) models was developed to predict the posttreatment central foveal thickness (CFT) and the best-corrected visual acuity (BCVA). A total of 363 OCT images and 7,587 clinical data records from 363 eyes were included in the training set (304 eyes) and external validation set (59 eyes). The DL models were trained using the OCT images, and the CML models were trained using the OCT images features and clinical variables. The predictive posttreatment CFT and BCVA values were compared with true outcomes obtained from the medical records.

Results

For CFT prediction, the mean absolute error (MAE), root mean square error (RMSE), and R² of the best-performing model in the training set was 66.59, 93.73, and 0.71, respectively, with an area under receiver operating characteristic curve (AUC) of 0.90 for distinguishing the eyes with good anatomical response. The MAE, RMSE, and R² was 68.08, 97.63, and 0.74, respectively, with an AUC of 0.94 in the external validation set. For BCVA prediction, the MAE, RMSE, and R² of the best-performing model in the training set was 0.19, 0.29, and 0.60, respectively, with an AUC of 0.80 for distinguishing eyes with a good functional response. The external validation achieved a MAE, RMSE, and R² of 0.13, 0.20, and 0.68, respectively, with an AUC of 0.81.

Conclusions

Our ensemble ML system accurately predicted posttreatment CFT and BCVA after anti-VEGF injections in DME patients, and can be used to prospectively assess the efficacy of anti-VEGF therapy in DME patients.

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Simple Summary

Radiomics has become a prominent component of medical imaging research and many studies show its specific value as a support tool for clinical decision-making processes. Radiomic data are typically analyzed with statistical and machine learning methods, which change depending on the disease context and the imaging modality. We found a certain bias in the literature towards the use of such methods and believe that this limitation may influence the capacity of producing accurate and reliable decisions. Therefore, in view of the relevance of various types of learning methods, we report their significance and discuss their unrevealed potential.

Abstract

Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential.