Abstract
Artificial intelligence (AI) and especially its subfield, machine learning (ML), have emerged as major fields of computer science research. ML has reportedly been effective in the diagnosis of keratoconus (KC) and its milder forms through the use of devices such as topographers, Scheimpflug imaging analysis, and anterior segment optical coherence tomography. In this chapter, we discuss the basic concepts related to the AI models used in KC diagnosis and share data from the papers published on the subject and the effectiveness of the methods employed.
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