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Comparison of different corneal imaging modalities using artificial intelligence for diagnosis of keratoconus: a systematic review and meta-analysis

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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.

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Authors and Affiliations

  1. Noor Research Center for Ophthalmic Epidemiology, Noor Eye Hospital, Tehran, Iran

    Hassan Hashemi

  2. Translational Ophthalmology Research Center, Tehran University of Medical Sciences, Tehran, Iran

    Farideh Doroodgar

  3. Negah Eye Hospital Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Farideh Doroodgar

  4. Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Sana Niazi

  5. Department of Medical Surgical Nursing, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Mehdi Khabazkhoob

  6. Department of Ophthalmology, Bu-Ali Sina Hospital, Mazandaran University of Medical Sciences, Sari, Iran

    Zahra Heidari

  7. Psychiatry and Behavioral Sciences Research Center, Mazandaran University of Medical Sciences, Sari, Iran

    Zahra Heidari

Authors
  1. Hassan Hashemi
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  2. Farideh Doroodgar
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  3. Sana Niazi
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  4. Mehdi Khabazkhoob
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  5. Zahra Heidari
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Contributions

Conceptualization: Hassan Hashemi, Farideh Doroodgar, and Zahra Heidari; methodology: Zahra Heidari and Mehdi Khabazkhoob; literature search and data analysis: Zahra Heidari and Mehdi Khabazkhoob; writing—original draft preparation: Zahra Heidari; review and editing: all authors; supervision: Hassan Hashemi. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Zahra Heidari.

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Hashemi, H., Doroodgar, F., Niazi, S. et al. Comparison of different corneal imaging modalities using artificial intelligence for diagnosis of keratoconus: a systematic review and meta-analysis. Graefes Arch Clin Exp Ophthalmol 262, 1017–1039 (2024). https://doi.org/10.1007/s00417-023-06154-6

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  • DOI: https://doi.org/10.1007/s00417-023-06154-6

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