ArticleJournal of biophotonics2018
Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning.
Article in Journal of biophotonics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management.Medicina (Kaunas, Lithuania) · 2022Pooled it
- A 48-Month, Prospective, Multicenter, Natural History Study of Choroideremia.Ophthalmology science · 2026Article
- Artificial intelligence applications in inherited retinal dystrophies.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Deep Learning-Assisted Measurements of Photoreceptor Ellipsoid Zone Area and Outer Segment Volume as Biomarkers for Retinitis Pigmentosa.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial intelligence in retinal disease: clinical application, challenges, and future directions.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Review
- The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.Biomedicines · 2023Review
- ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography.Frontiers in medicine · 2022Article
- Challenges Associated With Ellipsoid Zone Intensity Measurements Using Optical Coherence Tomography.Translational vision science & technology · 2021Article
- Emerging imaging developments in experimental vision sciences and ophthalmology.Experimental biology and medicine (Maywood, N.J.) · 2021Article
- Reliability of Subjective Assessment of Spectral-Domain OCT Pathologic Features by Multiple Raters in Retinal Vein Occlusion.Ophthalmology science · 2021Article
- Validating Ellipsoid Zone Area Measurement With Multimodal Imaging in Choroideremia.Translational vision science & technology · 2021Article
- Real-time retinal layer segmentation of OCT volumes with GPU accelerated inferencing using a compressed, low-latency neural network.Biomedical optics express · 2020Article
- MEDnet, a neural network for automated detection of avascular area in OCT angiography.Biomedical optics express · 2018Article
- Deep learning for the segmentation of preserved photoreceptors onBiomedical optics express · 2018Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
Abstract
Optical coherence tomography (OCT) can demonstrate early deterioration of the photoreceptor integrity caused by inherited retinal degeneration diseases (IRDs). A machine learning method based on random forests was developed to automatically detect continuous areas of preserved ellipsoid zone structure (an easily recognizable part of the photoreceptors on OCT) in 16 eyes of patients with choroideremia (a type of IRD). Pseudopodial extensions protruding from the preserved ellipsoid zone areas are detected separately by a local active contour routine. The algorithm is implemented on en face images with minimum segmentation requirements, only needing delineation of the Bruch's membrane, thus evading the inaccuracies and technical challenges associated with automatic segmentation of the ellipsoid zone in eyes with severe retinal degeneration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.