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Comparison of Different Corneal Imaging Modalities Using Artificial Intelligence for Diagnosis of Keratoconus: A Systematic Review and Meta-Analysis Publisher Pubmed



Hashemi H1 ; Doroodgar F2, 3 ; Niazi S4 ; Khabazkhoob M5 ; Heidari Z6, 7
Authors
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Authors Affiliations
  1. 1. Noor Research Center for Ophthalmic Epidemiology, Noor Eye Hospital, Tehran, Iran
  2. 2. Translational Ophthalmology Research Center, Tehran University of Medical Sciences, Tehran, Iran
  3. 3. Negah Eye Hospital Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  4. 4. Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  5. 5. Department of Medical Surgical Nursing, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  6. 6. Department of Ophthalmology, Bu-Ali Sina Hospital, Mazandaran University of Medical Sciences, Sari, Iran
  7. 7. Psychiatry and Behavioral Sciences Research Center, Mazandaran University of Medical Sciences, Sari, Iran

Source: Graefe's Archive for Clinical and Experimental Ophthalmology Published:2024


Abstract

Purpose: This review was designed to compare different corneal imaging modalities using artificial intelligence (AI) for the diagnosis of keratoconus (KCN), subclinical KCN (SKCN), and forme fruste KCN (FFKCN). Methods: A comprehensive systematic search was conducted in scientific databases, including Web of Science, PubMed, Scopus, and Google Scholar based on the PRISMA statement. Two independent reviewers assessed all potential publications on AI and KCN up to March 2022. The Critical Appraisal Skills Program (CASP) 11-item checklist was used to evaluate the validity of the studies. Eligible articles were categorized into three groups (KCN, SKCN, and FFKCN) and included in the meta-analysis. The pooled estimate of accuracy (PEA) was calculated for all selected articles. Results: The initial search yielded 575 relevant publications, of which 36 met the CASP quality criteria and were included in the analysis. Qualitative assessment showed that Scheimpflug and Placido combined with biomechanical and wavefront evaluations improved KCN detection (PEA, 99.2, and 99.0, respectively). The Scheimpflug system (92.25 PEA, 95% CI, 94.76–97.51) and a combination of Scheimpflug and Placido (96.44 PEA, 95% CI, 93.13–98.19) had the highest diagnostic accuracy for the detection of SKCN and FFKCN, respectively. The meta-analysis outcomes showed no significant difference between the CASP score and accuracy of the publications (all P > 0.05). Conclusions: Simultaneous Scheimpflug and Placido corneal imaging methods provide high diagnostic accuracy for early detection of keratoconus. The use of AI models improves the discrimination of keratoconic eyes from normal corneas. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.
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