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Corneal biomechanics in early diagnosis of keratoconus using artificial intelligence

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Abstract

Keratoconus is a blinding eye disease that affects activities of daily living; therefore, early diagnosis is crucial. Great efforts have been made toward an early diagnosis of keratoconus. Recent studies have shown that corneal biomechanics is associated with the occurrence and progression of keratoconus. Hence, detecting changes in corneal biomechanics may provide a novel strategy for early diagnosis. However, an early keratoconus diagnosis remains challenging due to the subtle and localized nature of its lesions. Artificial intelligence has been used to help address this problem. Herein, we reviewed the literature regarding three aspects of keratoconus (keratoconus, early keratoconus, and keratoconus grading) based on corneal biomechanical properties using artificial intelligence. Furthermore, we summarized the current research progress, limitations, and possible prospects.

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Abbreviations

AI:

Artificial intelligence

AUROC:

The area under the receiver-operating characteristic curve

CART:

Classification and regression tree

CH:

Corneal hysteresis

CRF:

Corneal resistance factor

FFKC:

Forme fruste keratoconus

ML:

Machine learning

RF:

Random forest

SKC:

Subclinical keratoconus

SVM:

Support vector machine

TBI:

Tomographic and Biomechanical Index

TKC:

Topographical keratoconus classification system

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Acknowledgements

The authors thank all the participants who made this study possible and Editage (www.editage.cn) for English-language editing assistance.

Author information

Author notes

  1. Yan Huo and Xuan Chen contributed equally to this work.

Authors and Affiliations

  1. School of Medicine, Nankai University, Tianjin, China

    Yan Huo, Xuan Chen & Yan Wang

  2. Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China

    Gauhar Ali Khan & Yan Wang

  3. Tianjin Eye Hospital, Tianjin Key Lab of Ophthalmology and Visual Science, Tianjin Eye Institute, Nankai University Affiliated Eye Hospital, 4 Gansu Road, He-ping District, Tianjin, 300020, China

    Yan Wang

  4. Nankai Eye Institute, Nankai University, Tianjin, China

    Yan Wang

Authors
  1. Yan Huo
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  2. Xuan Chen
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  3. Gauhar Ali Khan
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  4. Yan Wang
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Contributions

Conceptualization: Yan Huo, Xuan Chen; literature search: Yan Huo, Xuan Chen; data analysis: Yan Huo, Xuan Chen, Gauhar Ali Khan; draft preparation: Yan Huo, Xuan Chen, Gauhar Ali Khan; review editing: Yan Huo, Xuan Chen, Gauhar Ali Khan, Yan Wang. Supervision: Yan Wang; Project administration: Yan Wang. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Yan Wang.

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Conflict of interest

The authors declare no competing interests. This study was supported by the National Natural Science Foundation of China (No. 81873684 and 82271118).

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Huo, Y., Chen, X., Khan, G.A. et al. Corneal biomechanics in early diagnosis of keratoconus using artificial intelligence. Graefes Arch Clin Exp Ophthalmol 262, 1337–1349 (2024). https://doi.org/10.1007/s00417-023-06307-7

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