Optimizing Refractive Outcomes of SMILE: Artificial Intelligence versus Conventional State-of-the-Art Nomograms-Reference-Cited by-同舟云学术

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Optimizing Refractive Outcomes of SMILE: Artificial Intelligence versus Conventional State-of-the-Art Nomograms

Published:2023-11-30 Issue: Volume: Page:1-8
ISSN:0271-3683
Container-title:Current Eye Research
language:en
Short-container-title:Current Eye Research

Author:

Luft Nikolaus¹²,Mohr Niklas¹^ORCID,Spiegel Elmar³,Marchi Hannah³⁴,Siedlecki Jakob¹^ORCID,Harrant Lisa¹,Mayer Wolfgang J.¹^ORCID,Dirisamer Martin¹²,Priglinger Siegfried G.¹²

Affiliation:

1. Department of Ophthalmology University Hospital, LMU Munich, Munich, Germany

2. SMILE Eyes Clinic, Linz, Austria

3. Core Facility Statistical Consulting, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

4. Faculty of Business Administration and Economics, Bielefeld University, Bielefeld, Germany

Publisher

Informa UK Limited

Subject

Cellular and Molecular Neuroscience,Sensory Systems,Ophthalmology

Link

https://www.tandfonline.com/doi/pdf/10.1080/02713683.2023.2282938

Reference29 articles.

1. Artificial intelligence and deep learning in ophthalmology

2. Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology

3. Enhanced Tomographic Assessment to Detect Corneal Ectasia Based on Artificial Intelligence

4. Screening Candidates for Refractive Surgery With Corneal Tomographic–Based Deep Learning

5. Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography

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