SynthesisInternational ophthalmology2025
The application of artificial intelligence-based algorithms in predicting the progression of keratoconus: a systematic review.
Synthesis in International ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Prediction of Maximum Keratometry Progression With Stratified Progression Criteria in Keratoconus Patients Using Machine Learning.Translational vision science & technology · 2026Article
- A deep learning approach for keratoconus detection using spatio-temporal features from corneal imaging.Scientific reports · 2026Article
- Treatment-aware artificial intelligence for keratoconus progression prediction.International ophthalmology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo conduct a systematic review of studies examining the use of artificial intelligence (AI) algorithms in predicting the progression of keratoconus (KCN).
methodsA comprehensive review was conducted using PubMed, Google Scholar, and ScienceDirect, focusing on keywords related to AI and KCN progression. Only studies published in English that applied AI algorithms to evaluate changes in KCN were included. The QUADAS-2 tool was used to assess the risk of bias and the overall quality of the studies. A narrative qualitative synthesis approach was employed to summarize and interpret the findings.
resultsA total of ten studies involving 10,940 eyes were analyzed to evaluate the progression of KCN using various AI models, including machine learning and deep learning techniques. Most of these models demonstrated moderate to high discriminative ability, with reported area under the curve (AUC) values ranging from 0.77 to 0.93 and accuracies between 77.5% and 84.9%. Key predictive factors consistently identified across multiple studies included posterior elevation, maximum keratometry (Kmax), and younger age. Notably, eyes that progressed were significantly younger than those that remained stable. Furthermore, incorporating non-imaging clinical risk factors-such as IgE levels, eye rubbing, and deficiencies in vitamin D and B12-into the classification framework enhanced the performance of the models. However, none of the studies conducted external validation using an independent, multi-center cohort. Most relied on internal train-test splits or cross-validation for their analyses.
conclusionAI models show promise in identifying the progression of KCN. However, the current evidence is limited by inconsistent criteria for progression, insufficient external validation, dependence on specific devices, and a lack of comprehensive reporting on calibration and decision curve analysis. To integrate these tools into routine clinical practice, standardized definitions, multi-center prospective validation studies, and platform-independent algorithms are essential.
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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.