ArticleTranslational vision science & technology2019
Repeatability of Adaptive Optics Automated Cone Measurements in Subjects With Retinitis Pigmentosa and Novel Metrics for Assessment of Image Quality.
Article in Translational vision science & technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Adaptive optics retinal imaging in patients with usher syndrome.Frontiers in ophthalmology · 2024Article
- Characteristics of Rare Inherited Retinal Dystrophies in Adaptive Optics-A Study on 53 Eyes.Diagnostics (Basel, Switzerland) · 2023Article
- Intergrader agreement of foveal cone topography measured using adaptive optics scanning light ophthalmoscopy.Biomedical optics express · 2022Article
- Investigating Biomarkers forInternational journal of molecular sciences · 2022Article
- Impact of Reference Center Choice on Adaptive Optics Imaging Cone Mosaic Analysis.Investigative ophthalmology & visual science · 2022Article
- Short-Term Parafoveal Cone Loss Despite Preserved Ellipsoid Zone in Rod Cone Dystrophy.Translational vision science & technology · 2021Observational
- Theoretical versus empirical measures of retinal magnification for scaling AOSLO images.Journal of the Optical Society of America. A, Optics, image science, and vision · 2021Article
- Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy.Scientific reports · 2021Article
- Promises and pitfalls of evaluating photoreceptor-based retinal disease with adaptive optics scanning light ophthalmoscopy (AOSLO).Progress in retinal and eye research · 2021Review
- Fully Automated Estimation of Spacing and Density for Retinal Mosaics.Translational vision science & technology · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
purposeWe determine the intersession repeatability of cone measurements via flood-illuminated adaptive optics (AO) imaging in patients with retinitis pigmentosa (RP), to better differentiate variation due to imaging inaccuracies versus pathology-driven change.
methodsA total of 25 4° × 4° AO images were acquired three times on the same day in 10 subjects with RP, registered in i2K Retina, and cones were identified using a custom-built MATLAB algorithm. Nine equally spaced regions of interest were selected for each imaging set. A subset of subjectively "poor" and "good" quality images was selected by three independent graders, analyzed using cone density, cone location similarity (CLS) and cone spacing, and compared to age-matched normals.
resultsThe coefficient of variation (CoV), repeatability, and percent repeatability of automated cone density were slightly higher in patients with RP compared to age-matched normals, but showed no statistically significant difference. The standard deviation of CLS and cone spacing of nearest-neighbor distance demonstrated a statistically significant difference between good- and poor-quality images.
conclusionsRepeatability of automated cone density measurements in patients with RP is comparable to normals. Misidentification of cones due to image quality variability is a major limitation of automated cone counting algorithms in patients with RP. Our study suggests that CLS and cone spacing metrics could be used to help define image quality and, thus, increase confidence in automated cone counts in patients with RP. TRANSLATIONAL RELEVANCE: The novel AO image quality assessment metrics described in our study could help to improve patient image interpretation, prognosis, and longitudinal care.
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