ReviewFrontiers in big data2023
Big data in corneal diseases and cataract: Current applications and future directions.
Review in Frontiers in big data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Role of Neural Crest Cells in Establishing Corneal Transparency During Embryonic Development in Mice.Journal of cellular physiology · 2026Article
- Evaluation of Antimicrobial Peptide-Antibiotic Combination Treatment for Tackling Ocular and SystemicInternational journal of molecular sciences · 2026Article
- The Impact of Real-Time Data Analytics on Infection Prevention and Control Practices in Ophthalmology: A Nursing Perspective on Patient Outcomes and Cost-Effectiveness.Nursing & health sciences · 2026Article
- Transforming Eye-Care Diagnostics Through Artificial Intelligence, Biometric Evaluation, and Tele-Optometry.Cureus · 2026Review
- International corneal and ocular surface disease dataset for electronic health records.The British journal of ophthalmology · 2025Article
- Enhancing Clinical Data Infrastructure for AI Research: Comparative Evaluation of Data Management Architectures.Journal of medical Internet research · 2025Article
- Article
- Review
- Comparison of cellular, mechanical, and optical properties of different polymers for corneal tissue engineering.Iranian journal of basic medical sciences · 2025Article
- AI-driven strategies for advancing corneal cell therapy: a promising frontier.Frontiers in medicine · 2025Review
- Evaluation of ophthalmic healthcare professional-led keratoconus management service in the United Kingdom: the Birmingham and Midland Eye Centre (BMEC) study.Eye (London, England) · 2024Article
- Analyzing the changing trend of corneal biomechanical properties under different influencing factors in T2DM patients.Scientific reports · 2024Article
- Clinical Applications of Anterior Segment Optical Coherence Tomography: An Updated Review.Diagnostics (Basel, Switzerland) · 2024Review
- ChatGPT in ophthalmology: the dawn of a new era?Eye (London, England) · 2024Article
- The Royal College of Ophthalmologists' National Ophthalmology Database study of cataract surgery: report 16, influence of remuneration model on choice of intraocular lens in the UK.Eye (London, England) · 2023Article
- Animal Models in Eye Research: Focus on Corneal Pathologies.International journal of molecular sciences · 2023Review
- Diagnosis ofDiagnostics (Basel, Switzerland) · 2023Review
- Diagnostic performance of deep learning in infectious keratitis: a systematic review and meta-analysis protocol.BMJ open · 2023Article
- Management of keratoconus: an updated review.Frontiers in medicine · 2023Review
- A 7-year review of clinical characteristics, predisposing factors and outcomes of post-keratoplasty infectious keratitis: the Nottingham infectious keratitis study.Frontiers in cellular and infection microbiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of "5 Vs"-variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.
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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.