ArticlePloS one2018
Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- Artificial Intelligence in Ophthalmology: Current Status, Challenges, and Future Perspectives.Health care science · 2026Review
- Artificial intelligence for posterior capsule opacification.Frontiers in medicine · 2025Review
- Eye of the future: Unlocking the potential utilization of hydrogels in intraocular lenses.Bioengineering & translational medicine · 2024Review
- Applications of Artificial Intelligence in Cataract Surgery: A Review.Clinical ophthalmology (Auckland, N.Z.) · 2024Review
- Artificial intelligence in the anterior segment of eye diseases.International journal of ophthalmology · 2024Review
- Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023).International journal of ophthalmology · 2023Article
- Automated artificial intelligence-based system for clinical follow-up of patients with age-related macular degeneration.Acta ophthalmologica · 2022Article
- Application of artificial intelligence in cataract management: current and future directions.Eye and vision (London, England) · 2022Review
- Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) · 2021Review
- Detection of Fuchs' Uveitis Syndrome From Slit-Lamp Images Using Deep Convolutional Neural Networks in a Chinese Population.Frontiers in cell and developmental biology · 2021Article
- Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization.Annals of translational medicine · 2020Review
- Application of machine learning in ophthalmic imaging modalities.Eye and vision (London, England) · 2020Review
Corrections and comments
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Authors and funding
18 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ocular images play an essential role in ophthalmology. Current research mainly focuses on computer-aided diagnosis using slit-lamp images, however few studies have been done to predict the progression of ophthalmic disease. Therefore exploring an effective approach of prediction can help to plan treatment strategies and to provide early warning for the patients. In this study, we present an end-to-end temporal sequence network (TempSeq-Net) to automatically predict the progression of ophthalmic disease, which includes employing convolutional neural network (CNN) to extract high-level features from consecutive slit-lamp images and applying long short term memory (LSTM) method to mine the temporal relationship of features. First, we comprehensively compare six potential combinations of CNNs and LSTM (or recurrent neural network) in terms of effectiveness and efficiency, to obtain the optimal TempSeq-Net model. Second, we analyze the impacts of sequence lengths on model's performance which help to evaluate their stability and validity and to determine the appropriate range of sequence lengths. The quantitative results demonstrated that our proposed model offers exceptional performance with mean accuracy (92.22), sensitivity (88.55), specificity (94.31) and AUC (97.18). Moreover, the model achieves real-time prediction with only 27.6ms for single sequence, and simultaneously predicts sequence data with lengths of 3-5. Our study provides a promising strategy for the progression of ophthalmic disease, and has the potential to be applied in other medical fields.
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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.