Abstract
AbstractFrom Nottingham’s original treatise on keratoconus to the more recent incorporation of artificial intelligence within the clinical space, corneal specialists have been at the forefront of developing and utilizing algorithmic approaches for the diagnosis and treatment of keratoconus. Although surgeon experience remains an essential component in any decision, combining the rapidly increasing number of clinical and diagnostic variables with complex computational programs may further extend the ability to accurately diagnose keratoconus, predict the likely progression of the disease, and determine the need to implement therapeutic and surgical treatments to optimize both short- and long-term visual acuity and safety outcomes. In this chapter, we discuss the development of algorithmic approaches over time within the keratoconus diagnostic and treatment paradigm discussing the introduction and development of machine learning approaches.KeywordsDecision treeMachine learningDiagnosisManagementArtificial intelligence
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