ArticleTranslational vision science & technology2025
Using Artificial Intelligence for an Efficient Prediction of Outcomes of Deep Anterior Lamellar Keratoplasty (DALK) in Advanced Keratoconus.
Article in Translational vision science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence for Predicting Postoperative Outcomes in Anterior Segment and Ocular Adnexa Surgery: A Systematic Review.Ophthalmology science · 2026Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To identify and analyze clinical risk factors and imaging parameters influencing the outcomes of deep anterior lamellar keratoplasty (DALK) for advanced keratoconus (KC) using an artificial intelligence (AI) model. Methods: This study included 250 DALK eyes with a 5-year follow-up for advanced KC. The DALK eyes were classified as having "favorable" or "unfavorable" outcomes based on graft clarity, scarring at the graft-host interface involving the visual axis which was not pre-existing, early suture loosening less than 3 months after the surgery, corneal vascularization reaching up to or into the graft-host junction at any follow up period, persistent corneal edema greater than 3 months after surgery, and change in visual acuity. Clinical risk factors were determined through a detailed clinical evaluation and questionnaire assessment and included the presence of systemic allergy, ocular allergy, or eye rubbing. Immunoglobulin E (IgE) and vitamin D and B12 levels were obtained from blood investigations. A total of 37 tomographic parameters were exported from an OCULUS Pentacam HR. An AI model was then built to assess these risk factors and imaging parameters. The area under the curve (AUC) and other metrics were evaluated. Results: The AI model classified 92.2% and 89.4% cases as favorable or unfavorable, respectively, based on clinical risk factors and imaging parameters. Systemic allergy, IgE, eye rubbing, and vitamin D had the highest information gains followed by posterior corneal data from the Pentacam HR. The AI model achieved an AUC of 0.957 with sensitivity of 98% and specificity of 85.6%. Conclusions: Our findings demonstrate the importance of preoperative risk stratification, which can affect surgical outcomes of DALK using AI. Translational Relevance: Better identification and control of these factors would enable better management and outcomes of DALK for advanced KC.
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Registered trials
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