ReviewClinical & experimental ophthalmology2022
Automation of dry eye disease quantitative assessment: A review.
Review in Clinical & experimental ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Comparing automated and manual assessments of tear break-up time using different non-invasive devices and a fluorescein procedure.Scientific reports · 2024Trial
- Determination of the Non-Invasive Tear Break-Up Time Cut-Off Point for Diagnosis of Dry Eye Disease and Its Correlation with Other Dry Eye Tests.Turkish journal of ophthalmology · 2026Article
- Experts Perspectives on Current Practices and Challenges of Diagnosis and Treatment of Dry Eye Disease across Europe and Central Asia.Ophthalmology and therapy · 2026Article
- Application of a deep learning-based system for eyelid margin signs identification training and testing in dry eye disease education.Frontiers in medicine · 2026Article
- An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease.Scientific reports · 2024Article
- Automated tear film break-up time measurement for dry eye diagnosis using deep learning.Scientific reports · 2024Article
- Advancements in Nanogels for Enhanced Ocular Drug Delivery: Cutting-Edge Strategies to Overcome Eye Barriers.Gels (Basel, Switzerland) · 2023Review
- Dynamic Aspects of Pre-Soft Contact Lens Tear Film and Their Relation to Dry Eye: Basic Science and Clinical Relevance.Life (Basel, Switzerland) · 2023Review
- The efficacy of ocular surface assessment approaches in evaluating dry eye treatment with artificial tears.Scientific reports · 2022Article
- Automation of dry eye disease quantitative assessment: A review.Clinical & experimental ophthalmology · 2022Review
- Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.Journal of current ophthalmologyReview
- Facial artificial intelligence in ophthalmology and medicine: fundamental and transformative applications.Therapeutic advances in ophthalmologyReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dry eye disease (DED) is a common eye condition worldwide and a primary reason for visits to the ophthalmologist. DED diagnosis is performed through a combination of tests, some of which are unfortunately invasive, non-reproducible and lack accuracy. The following review describes methods that diagnose and measure the extent of eye dryness, enabling clinicians to quantify its severity. Our aim with this paper is to review classical methods as well as those that incorporate automation. For only four ways of quantifying DED, we take a deeper look into what main elements can benefit from automation and the different ways studies have incorporated it. Like numerous medical fields, Artificial Intelligence (AI) appears to be the path towards quality DED diagnosis. This review categorises diagnostic methods into the following: classical, semi-automated and promising AI-based automated methods.
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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.