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Keratoconus is a common disorder. An early diagnosis influences the disease prognosis in the affected patients and prevents postoperative complications in patients with keratoconus considering refractive surgery. Machine learning approaches have been widely used for image classification. Here, we will assess the ability of deep learning to enable high-performance image classification of the color-coded corneal maps obtained by Scheimpflug camera in patients with keratoconus, subclinical keratoconus, and normal individuals.
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Inclusion criteria
Keratoconus group:
Suspicious group:
• Defined as subtle corneal tomographic changes as the aforementioned keratoconus abnormalities in the absence of slit- lamp or visual acuity changes typical of keratoconus (forme fruste keratoconus).
Normal group:
Exclusion criteria
1,669 participants in 3 patient groups
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Data sourced from clinicaltrials.gov
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