Trial reportOphthalmology2020
Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.
Trial report in Ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01949324 (A Phase 2 Multicenter Randomized Clinical Trial of Ciliary Neurotrophic Factor), which is not on this map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
A Phase 2 Multicenter Randomized Clinical Trial of Ciliary Neurotrophic Factor (CNTF) for Macular Telangiectasia Type 2 (MacTel)
Who cites it
21 citing papers in PubMed, 34 citations in OpenAlex.
- Impact of Baseline Quantitative OCT Features on Response to Risuteganib for the Treatment of Dry Age-Related Macular Degeneration: The Importance of Outer Retinal Integrity.Ophthalmology. Retina · 2022Trial
- 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
- Self-knowledge distillation-empowered directional connectivity transformer for microbial keratitis biomarkers segmentation on slit-lamp photography.Medical image analysis · 2025Article
- Validation of Inter-Reader Agreement/Consistency for Quantification of Ellipsoid Zone Integrity and Sub-RPE Compartmental Features Across Retinal Diseases.Diagnostics (Basel, Switzerland) · 2024Article
- Unraveling the mysteries of macular telangiectasia 2: the intersection of philanthropy, multimodal imaging and molecular genetics. The 2022 founders lecture of the pan American vitreoretinal society.International journal of retina and vitreous · 2023Review
- FemurTumorNet: Bone tumor classification in the proximal femur using DenseNet model based on radiographs.Journal of bone oncology · 2023Article
- Joint Multimodal Deep Learning-based Automatic Segmentation of Indocyanine Green Angiography and OCT Images for Assessment of Polypoidal Choroidal Vasculopathy Biomarkers.Ophthalmology science · 2023Article
- Deep Learning Algorithm Detects Presence of Disorganization of Retinal Inner Layers (DRIL)-An Early Imaging Biomarker in Diabetic Retinopathy.Translational vision science & technology · 2023Article
- Bibliometric analysis of artificial intelligence and optical coherence tomography images: research hotspots and frontiers.International journal of ophthalmology · 2023Article
- Artificial Intelligence for COVID-19 Detection in Medical Imaging-Diagnostic Measures and Wasting-A Systematic Umbrella Review.Journal of clinical medicine · 2022Review
- Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2.The British journal of ophthalmology · 2022Article
- LONGITUDINAL ELLIPSOID ZONE AND OUTER RETINAL INTEGRITY DYNAMICS AFTER EPIRETINAL MEMBRANE SURGERY.Retina (Philadelphia, Pa.) · 2022Article
- Open-source deep learning-based automatic segmentation of mouse Schlemm's canal in optical coherence tomography images.Experimental eye research · 2022Article
- The assessment of fundus image quality labeling reliability among graders with different backgrounds.PloS one · 2022Article
- Automatic quantification of retinal photoreceptor integrity to predict persistent disease activity in neovascular age-related macular degeneration using deep learning.Frontiers in neuroscience · 2022Article
- Deep Learning-Based Automatic Detection of Ellipsoid Zone Loss in Spectral-Domain OCT for Hydroxychloroquine Retinal Toxicity Screening.Ophthalmology science · 2021Article
- Challenges Associated With Ellipsoid Zone Intensity Measurements Using Optical Coherence Tomography.Translational vision science & technology · 2021Article
- Open-Source Automatic Biomarker Measurement on Slit-Lamp Photography to Estimate Visual Acuity in Microbial Keratitis.Translational vision science & technology · 2021Article
- Adaptive optics: principles and applications in ophthalmology.Eye (London, England) · 2021Review
- Open-Source Automatic Segmentation of Ocular Structures and Biomarkers of Microbial Keratitis on Slit-Lamp Photography Images Using Deep Learning.IEEE journal of biomedical and health informatics · 2021Article
Corrections and comments
- Commented on by
Authors and funding
6 authors at 5 institutions in 1 country.
Funding
Abstract
purposeTo validate the efficacy of a fully automatic, deep learning-based segmentation algorithm beyond conventional performance metrics by measuring the primary outcome of a clinical trial for macular telangiectasia type 2 (MacTel2).
designEvaluation of diagnostic test or technology.
participantsA total of 92 eyes from 62 participants with MacTel2 from a phase 2 clinical trial (NCT01949324) randomized to 1 of 2 treatment groups
methodsThe ellipsoid zone (EZ) defect areas were measured on spectral domain OCT images of each eye at 2 time points (baseline and month 24) by a fully automatic, deep learning-based segmentation algorithm. The change in EZ defect area from baseline to month 24 was calculated and analyzed according to the clinical trial protocol.
main outcome measureDifference in the change in EZ defect area from baseline to month 24 between the 2 treatment groups.
resultsThe difference in the change in EZ defect area from baseline to month 24 between the 2 treatment groups measured by the fully automatic segmentation algorithm was 0.072±0.035 mm
conclusionsThe fully automatic segmentation algorithm was as accurate as semiautomatic expert segmentation to assess EZ defect areas and was able to reliably reproduce the statistically significant primary outcome measure of the clinical trial. This approach, to validate the performance of an automatic segmentation algorithm on the primary clinical trial end point, provides a robust gauge of its clinical applicability.
Indexed as
Identifiers
What OpenQuestion holds
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.