Keratoconus (KC) is an inflammatory disorder causing asymmetrical and progressive corneal ectasia, which typically presents in early adolescence and progresses into the second or third decades of life.
1 The major risk factors for KC include eye rubbing, a history of ocular and systemic allergy including atopy, and a family history of KC.
2 Early detection of KC is important for better treatment outcomes, as timely intervention with procedures such as corneal cross-linking may avoid more invasive treatments such as corneal transplantation.
3 For the advanced KC eye, deep anterior lamellar keratoplasty (DALK) can be performed.
4–6 It is an alternative to full-thickness penetrating keratoplasty for the treatment of KC and has several advantages over penetrating keratoplasty, such as a lower risk of graft rejection and preservation of endothelial cell density.
7–9 This technique can significantly improve the vision-related quality of life in patients, and studies have reported visual outcomes comparable to penetrating keratoplasty.
10–12 However, DALK is a technically more challenging procedure to perform compared to penetrating keratoplasty and requires a greater level of surgical expertise.
13 Although there are various intra- and postoperative factors that can alter the outcomes of DALK, the predictive correlations between demographic and clinical risk factors such as systemic allergy, ocular allergy, and eye rubbing are unknown. Therefore, we aimed to use artificial intelligence (AI) to integrate and stratify various preoperative clinical risk factors and imaging parameters from a Pentacam HR (OCULUS, Wetzlar, Germany), which can predict long-term outcomes of DALK.
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