ArticleInvestigative ophthalmology & visual science2019
Treatment Potential for Macular Cone Vision in Leber Congenital Amaurosis Due to CEP290 or NPHP5 Mutations: Predictions From Artificial Intelligence.
Article in Investigative ophthalmology & visual science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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
23 citing papers in PubMed, 36 citations in OpenAlex.
- Recovery of cone-mediated vision in Lebercilin associated retinal ciliopathy after gene therapy: One-year results of a phase I/II trial.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Trial
- Gene Editing forThe New England journal of medicine · 2024Trial
- Leveraging large scale deep learning models for diagnosis and visual outcome prediction in retinitis pigmentosa.NPJ digital medicine · 2026Article
- Location and Identity of Photoreceptors Contributing to the Full-Field Stimulus Test (FST).Investigative ophthalmology & visual science · 2025Review
- Evaluating the Progression of Retinal Sensitivity Loss in Geographic Atrophy Using Machine-Learning-Based Structure-Function Correlation (OMEGA 2).Investigative ophthalmology & visual science · 2025Article
- Inherited Retinal Degenerations and Non-Neovascular Age-Related Macular Degeneration: Progress and Unmet Needs.Translational vision science & technology · 2024Review
- Update on Clinical Trial Endpoints in Gene Therapy Trials for Inherited Retinal Diseases.Journal of clinical medicine · 2024Review
- The Extraordinary Phenotypic and Genetic Variability of Retinal and Macular Degenerations: The Relevance to Therapeutic Developments.Cold Spring Harbor perspectives in medicine · 2024Review
- Full-field stimulus threshold testing: a scoping review of current practice.Eye (London, England) · 2024Article
- Durable vision improvement after a single intravitreal treatment with antisense oligonucleotide inAmerican journal of ophthalmology case reports · 2023Article
- Treatment Strategy With Gene Editing for Late-Onset Retinal Degeneration Caused by a Founder Variant in C1QTNF5.Investigative ophthalmology & visual science · 2023Article
- Artificial intelligence-based strategies to identify patient populations and advance analysis in age-related macular degeneration clinical trials.Experimental eye research · 2022Article
- Restoration of Cone Sensitivity to Individuals with Congenital Photoreceptor Blindness within the Phase 1/2 Sepofarsen Trial.Ophthalmology science · 2022Article
- Estimation of current and post-treatment retinal function in chronic central serous chorioretinopathy using artificial intelligence.Scientific reports · 2021Article
- Bardet-Biedl syndrome-7 (Ophthalmic genetics · 2021Article
- Fundus-controlled perimetry (microperimetry): Application as outcome measure in clinical trials.Progress in retinal and eye research · 2021Review
- Durable vision improvement after a single treatment with antisense oligonucleotide sepofarsen: a case report.Nature medicine · 2021Article
- Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective.Progress in retinal and eye research · 2021Review
- Progress in treating inherited retinal diseases: Early subretinal gene therapy clinical trials and candidates for future initiatives.Progress in retinal and eye research · 2020Review
- Advancing Clinical Trials for Inherited Retinal Diseases: Recommendations from the Second Monaciano Symposium.Translational vision science & technology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Purpose: To use supervised machine learning to predict visual function from retinal structure in retinitis pigmentosa (RP) and apply these estimates to CEP290- and NPHP5-associated Leber congenital amaurosis (LCA) to determine the potential for functional improvement. Methods: Patients with RP (n = 20) and LCA due to CEP290 (n = 12) or NPHP5 (n = 6) mutations were studied. A patient with CEP290 mutations but mild retinal degeneration was included. RP patients had cone-mediated macular function. A machine learning technique was used to associate perimetric sensitivities to local structure in RP patients. Models trained on RP data were applied to predict visual function in LCA. Results: The RP and LCA patients had comparable retinal structure. RP patients had peak sensitivity at the fovea surrounded by decreasing sensitivity. Machine learning could successfully predict perimetry results from segmented or unsegmented optical coherence tomography (OCT) input. Application of machine learning predictions to LCA within the residual macular island of photoreceptor structure showed differences between predicted and measured sensitivities defining treatment potential. In patients with retained vision, the treatment potential was 4.6 ± 2.9 dB at the fovea but 16.4 ± 4.4 dB at the parafovea. In patients with limited or no vision, the treatment potential was 17.6 ± 9.4 dB. Conclusions: Cone vision improvement potential in LCA due to CEP290 or NPHP5 mutations is predictable from retinal structure using a machine learning approach. This should allow individual prediction of the maximal efficacy in clinical trials and guide decisions about dosing. Similar strategies can be used in other retinal degenerations to estimate the extent and location of treatment potential.
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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.