Observational studyOphthalmology2018
Clinical Prediction Performance of Glaucoma Progression Using a 2-Dimensional Continuous-Time Hidden Markov Model with Structural and Functional Measurements.
Observational study in Ophthalmology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 15 citations in OpenAlex.
- A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.NPJ digital medicine · 2026Article
- Article
- OCT Optic Nerve Head Morphology in Myopia II: Peri-Neural Canal Scleral Bowing and Choroidal Thickness in High Myopia-An American Ophthalmological Society Thesis.American journal of ophthalmology · 2023Article
- A deep-learning system predicts glaucoma incidence and progression using retinal photographs.The Journal of clinical investigation · 2022Article
- Evaluation of the external validity of a joint structure-function model for monitoring glaucoma progression.Scientific reports · 2020Article
- Peripapillary Scleral Bowing Increases with Age and Is Inversely Associated with Peripapillary Choroidal Thickness in Healthy Eyes.American journal of ophthalmology · 2020Article
- Machine learning classifiers-based prediction of normal-tension glaucoma progression in young myopic patients.Japanese journal of ophthalmology · 2020Article
- Forecasting Retinal Nerve Fiber Layer Thickness from Multimodal Temporal Data Incorporating OCT Volumes.Ophthalmology. GlaucomaObservational
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
purposePreviously, we introduced a state-based 2-dimensional continuous-time hidden Markov model (2D CT HMM) to model the pattern of detected glaucoma changes using structural and functional information simultaneously. The purpose of this study was to evaluate the detected glaucoma change prediction performance of the model in a real clinical setting using a retrospective longitudinal dataset.
designLongitudinal, retrospective study.
participantsOne hundred thirty-four eyes from 134 participants diagnosed with glaucoma or as glaucoma suspects (average follow-up, 4.4±1.2 years; average number of visits, 7.1±1.8).
methodsA 2D CT HMM model was trained using OCT (Cirrus HD-OCT; Zeiss, Dublin, CA) average circumpapillary retinal nerve fiber layer (cRNFL) thickness and visual field index (VFI) or mean deviation (MD; Humphrey Field Analyzer; Zeiss). The model was trained using a subset of the data (107 of 134 eyes [80%]) including all visits except for the last visit, which was used to test the prediction performance (training set). Additionally, the remaining 27 eyes were used for secondary performance testing as an independent group (validation set). The 2D CT HMM predicts 1 of 4 possible detected state changes based on 1 input state.
main outcome measuresPrediction accuracy was assessed as the percentage of correct prediction against the patient's actual recorded state. In addition, deviations of the predicted long-term detected change paths from the actual detected change paths were measured.
resultsBaseline mean ± standard deviation age was 61.9±11.4 years, VFI was 90.7±17.4, MD was -3.50±6.04 dB, and cRNFL thickness was 74.9±12.2 μm. The accuracy of detected glaucoma change prediction using the training set was comparable with the validation set (57.0% and 68.0%, respectively). Prediction deviation from the actual detected change path showed stability throughout patient follow-up.
conclusionsThe 2D CT HMM demonstrated promising prediction performance in detecting glaucoma change performance in a simulated clinical setting using an independent cohort. The 2D CT HMM allows information from just 1 visit to predict at least 5 subsequent visits with similar performance.
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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.