ArticleBiomedical optics express2018
Deep learning for the segmentation of preserved photoreceptors on
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management.Medicina (Kaunas, Lithuania) · 2022Pooled it
- VALIDATION OF A DEEP LEARNING-BASED ALGORITHM FOR SEGMENTATION OF THE ELLIPSOID ZONE ON OPTICAL COHERENCE TOMOGRAPHY IMAGES OF AN USH2A-RELATED RETINAL DEGENERATION CLINICAL TRIAL.Retina (Philadelphia, Pa.) · 2022Trial
- Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.Ophthalmology · 2020Trial
- 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-Based Retinal Layer Segmentation in Optical Coherence Tomography Scans of Patients with Inherited Retinal Diseases.Klinische Monatsblatter fur Augenheilkunde · 2025Article
- Quantification of Optical Coherence Tomography Features in >3500 Patients with Inherited Retinal Disease Reveals Novel Genotype-Phenotype Associations.medRxiv : the preprint server for health sciences · 2025Article
- Article
- 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
- Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images.The British journal of ophthalmology · 2023Article
- The Natural History ofInternational journal of molecular sciences · 2022Article
- Deep learning-based facial image analysis in medical research: a systematic review protocol.BMJ open · 2021Article
- Artificial intelligence in OCT angiography.Progress in retinal and eye research · 2021Review
- Artificial Intelligence-Assisted Early Detection of Retinitis Pigmentosa - the Most Common Inherited Retinal Degeneration.Journal of digital imaging · 2021Article
- Deep Learning Applied to Automated Segmentation of Geographic Atrophy in Fundus Autofluorescence Images.Translational vision science & technology · 2021Article
- Prospective deep phenotyping of choroideremia patients using multimodal structure-function approaches.Eye (London, England) · 2021Article
- Real-time retinal layer segmentation of OCT volumes with GPU accelerated inferencing using a compressed, low-latency neural network.Biomedical optics express · 2020Article
- Automated segmentation of peripapillary retinal boundaries in OCT combining a convolutional neural network and a multi-weights graph search.Biomedical optics express · 2019Article
- MEDnet, a neural network for automated detection of avascular area in OCT angiography.Biomedical optics express · 2018Article
- Choroideremia: from genetic and clinical phenotyping to gene therapy and future treatments.Therapeutic advances in ophthalmologyReview
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The objective quantification of photoreceptor loss in inherited retinal degenerations (IRD) is essential for measuring disease progression, and is now especially important with the growing number of clinical trials. Optical coherence tomography (OCT) is a non-invasive imaging technology widely used to recognize and quantify such anomalies. Here, we implement a versatile method based on a convolutional neural network to segment the regions of preserved photoreceptors in two different IRDs (choroideremia and retinitis pigmentosa) from OCT images. An excellent segmentation accuracy (~90%) was achieved for both IRDs. Due to the flexibility of this technique, it has potential to be extended to additional IRDs in the future.
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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.