ArticleOphthalmology science
Keratoconus Detection-based on Dynamic Corneal Deformation Videos Using Deep Learning.
Article in Ophthalmology science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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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.
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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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Reply.Ophthalmology science · 2026Article
- ChatGPT in precision medicine.APL bioengineering · 2026Review
- A Deep Learning-Based Graphical User Interface for Predicting Corneal Ectasia Scores from Raw Optical Coherence Tomography Data.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Derived Biomechanical Index for Early Corneal Ectasia Detection: Advancing Beyond Tomography.Ophthalmology science · 2026Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Deep Learning-Based Diagnosis of Corneal Condition by Using Raw Optical Coherence Tomography Data.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning-based keratoconus detection from Scheimpflug images.Biomedical optics express · 2025Article
- Artificial intelligence derived grading of mustard gas induced corneal injury and opacity.Scientific reports · 2025Article
- Screening with the Bilateral Corneal Symmetry 3-D Analyzer.International journal of environmental research and public health · 2025Article
- Advances in machine learning for keratoconus diagnosis.International ophthalmology · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To assess the performance of convolutional neural networks (CNNs) for automated detection of keratoconus (KC) in standalone Scheimpflug-based dynamic corneal deformation videos. Design: Retrospective cohort study. Participants: We retrospectively analyzed datasets with records of 734 nonconsecutive, refractive surgery candidates, and patients with unilateral or bilateral KC. Methods: We first developed a video preprocessing pipeline to translate dynamic corneal deformation videos into 3-dimensional pseudoimage representations and then trained a CNN to directly identify KC from pseudoimages. We calculated the model's KC probability score cut-off and evaluated the performance by subjective and objective accuracy metrics using 2 independent datasets. Main Outcome Measures: Area under the receiver operating characteristics curve (AUC), accuracy, specificity, sensitivity, and KC probability score. Results: The model accuracy on the test subset was 0.89 with AUC of 0.94. Based on the external validation dataset, the AUC and accuracy of the CNN model for detecting KC were 0.93 and 0.88, respectively. Conclusions: Our deep learning-based approach was highly sensitive and specific in separating normal from keratoconic eyes using dynamic corneal deformation videos at levels that may prove useful in clinical practice. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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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.