Determinants of Cone and Rod Functions in Geographic Atrophy: AI-Based Structure-Function Correlation

Pfau, Maximilian; Emde, Leon von der; Dysli, Chantal; Möller, Philipp T.; Thiele, Sarah; Lindner, Moritz; Schmid, Matthias; Rubin, Daniel L.; Fleckenstein, Monika; Holz, Frank G.; Schmitz-Valckenberg, Steffen

doi:10.1016/j.ajo.2020.04.003

Cited by 39 publications

(64 citation statements)

References 27 publications

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“…A synopsis of the literature can be found in Table 3 . 23 – 49 The complete review dataset, which included in-depth information regarding all computing processes used and results obtained, can be found in Supplementary Table S1 .…”

Section: Resultsmentioning

confidence: 99%

“…With the outcome set to VRQOL, they found predictors such as best-corrected visual acuity of the better eye, low-luminance visual acuity (LLVA) for the better eye, GA size, foveal sparing status, and LLVA for the worst eye yielded a model with an R 2 of 0.32. Pfau et al 49 identified predictors of retinal sensitivity based on the retinal microstructure in the presence of GA for their predictive model that used random forest models with 1000 trees. For their outcome metrics, they used the mean absolute error (MAE), which served as a prediction accuracy measure.…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation

Arslan

Samarasinghe

Benke

et al. 2020

Trans. Vis. Sci. Tech.

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Purpose The purpose of this study was to summarize and evaluate artificial intelligence (AI) algorithms used in geographic atrophy (GA) diagnostic processes (e.g. isolating lesions or disease progression). Methods The search strategy and selection of publications were both conducted in accordance with the Preferred of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Web of Science were used to extract literary data. The algorithms were summarized by objective, performance, and scope of coverage of GA diagnosis (e.g. lesion automation and GA progression). Results Twenty-seven studies were identified for this review. A total of 18 publications focused on lesion segmentation only, 2 were designed to detect and classify GA, 2 were designed to predict future overall GA progression, 3 focused on prediction of future spatial GA progression, and 2 focused on prediction of visual function in GA. GA-related algorithms reported sensitivities from 0.47 to 0.98, specificities from 0.73 to 0.99, accuracies from 0.42 to 0.995, and Dice coefficients from 0.66 to 0.89. Conclusions Current GA-AI publications have a predominant focus on lesion segmentation and a minor focus on classification and progression analysis. AI could be applied to other facets of GA diagnoses, such as understanding the role of hyperfluorescent areas in GA. Using AI for GA has several advantages, including improved diagnostic accuracy and faster processing speeds. Translational Relevance AI can be used to quantify GA lesions and therefore allows one to impute visual function and quality-of-life. However, there is a need for the development of reliable and objective models and software to predict the rate of GA progression and to quantify improvements due to interventions.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation

Arslan

Samarasinghe

Benke

et al. 2020

Trans. Vis. Sci. Tech.

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“…3), while the RPE and IS&OS thicknesses were shown to represent the most important predictive imaging parameters. In accordance with previous studies in AMD 22,23 , we used the term 'inferred sensitivity' for this machine learning-based analysis strategy. It may serve as a quasi-functional surrogate marker in future clinical trials.…”

Section: Discussionmentioning

confidence: 99%

“…The smaller benefit (in comparison to AMD) 22,23 of adding patient-and eye-specific data to the trainingset may be linked to the overall more homogeneous patient cohort in STGD1. In contrast to previous work in AMD 22 , eye characteristics not reflected by OCT imaging (e.g., lenticular opacification) are much less likely to play a major role given the age of the patients.…”

Section: Discussionmentioning

confidence: 99%

“…The preparation and acquisition time is short (i.e., no pupil dilation needed), and the imaging does not require long periods of subject engagement and alertness 21 . Recently, the possibility to predict FCP results based on structural OCT data (also termed 'inferred sensitivity') using artificial intelligence algorithms, including machine learning techniques like random forest regression, was described for AMD 22,23 . In the view of upcoming therapeutic trials and the attempt to keep study protocols slender while achieving maximal validity and power 24 , the prediction of reliable functional outcome measures based on fast and routine retinal imaging might be a reasonable solution.…”

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confidence: 99%

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Inferred retinal sensitivity in recessive Stargardt disease using machine learning

Müller

Odainic

Treis

et al. 2021

Sci Rep

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Spatially-resolved retinal function can be measured by psychophysical testing like fundus-controlled perimetry (FCP or ‘microperimetry’). It may serve as a performance outcome measure in emerging interventional clinical trials for macular diseases as requested by regulatory agencies. As FCP constitute laborious examinations, we have evaluated a machine-learning-based approach to predict spatially-resolved retinal function (’inferred sensitivity’) based on microstructural imaging (obtained by spectral domain optical coherence tomography) and patient data in recessive Stargardt disease. Using nested cross-validation, prediction accuracies of (mean absolute error, MAE [95% CI]) 4.74 dB [4.48–4.99] were achieved. After additional inclusion of limited FCP data, the latter reached 3.89 dB [3.67–4.10] comparable to the test–retest MAE estimate of 3.51 dB [3.11–3.91]. Analysis of the permutation importance revealed, that the IS&OS and RPE thickness were the most important features for the prediction of retinal sensitivity. ’Inferred sensitivity’, herein, enables to accurately estimate differential effects of retinal microstructure on spatially-resolved function in Stargardt disease, and might be used as quasi-functional surrogate marker for a refined and time-efficient investigation of possible functionally relevant treatment effects or disease progression.

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Progression of Photoreceptor Degeneration in Geographic Atrophy Secondary to Age-related Macular Degeneration

et al. 2020

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IMPORTANCESensitive outcome measures for disease progression are needed for treatment trials in geographic atrophy (GA) secondary to age-related macular degeneration (AMD).OBJECTIVE To quantify photoreceptor degeneration outside regions of GA in eyes with nonexudative AMD, to evaluate its association with future GA progression, and to characterize its spatio-temporal progression. Monocenter cohort study (Directional Spread in Geographic Atrophy [NCT02051998]) and analysis of data from a normative data study at a tertiary referral center. One hundred fifty-eight eyes of 89 patients with a mean (SD) age of 77.7 (7.1) years, median area of GA of 8.87 mm 2 (IQR, 4.09-15.60), and median follow-up of 1.1 years (IQR, 0.52-1.7 years), as well as 93 normal eyes from 93 participants. DESIGN, SETTING, AND PARTICIPANTSEXPOSURES Longitudinal spectral-domain optical coherence tomography (SD-OCT) volume scans (121 B-scans across 30°× 25°) were segmented with a deep-learning pipeline and standardized in a pointwise manner with age-adjusted normal data (z scores). Outer nuclear layer (ONL), photoreceptor inner segment (IS), and outer segment (OS) thickness were quantified along evenly spaced contour lines surrounding GA lesions. Linear mixed models were applied to assess the association between photoreceptor-related imaging features and GA progression rates and characterize the pattern of photoreceptor degeneration over time.MAIN OUTCOMES AND MEASURES Association of ONL thinning with follow-up time (after adjusting for age, retinal topography [z score], and distance to the GA boundary). RESULTSThe study included 158 eyes of 89 patients (51 women and 38 men) with a mean (SD) age of 77.7 (7.1) years. The fully automated B-scan segmentation was accurate (dice coefficient, 0.82; 95% CI, 0.80-0.85; compared with manual markings) and revealed a marked interpatient variability in photoreceptor degeneration. The ellipsoid zone (EZ) loss-to-GA boundary distance and OS thickness were prognostic for future progression rates. Outer nuclear layer and IS thinning over time was significant even when adjusting for age and proximity to the GA boundary (estimates of −0.16 μm/y; 95% CI, −0.30 to −0.02; and −0.17 μm/y; 95% CI, −0.26 to −0.09).CONCLUSIONS AND RELEVANCE Distinct and progressive alterations of photoreceptor laminae (exceeding GA spatially) were detectable and quantifiable. The degree of photoreceptor degeneration outside of regions of retinal pigment epithelium atrophy varied markedly between eyes and was associated with future GA progression. Macula-wide photoreceptor laminae thinning represents a potential candidate end point to monitor treatment effects beyond mere GA lesion size progression.

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Determinants of Cone and Rod Functions in Geographic Atrophy: AI-Based Structure-Function Correlation

Cited by 39 publications

References 27 publications

Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation

Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation

Inferred retinal sensitivity in recessive Stargardt disease using machine learning

Progression of Photoreceptor Degeneration in Geographic Atrophy Secondary to Age-related Macular Degeneration

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