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Artificial Intelligence in Refractive Surgery

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Artificial Intelligence in Ophthalmology

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Abstract

Artificial intelligence has become a valuable extension of refractive surgery since it is capable of assisting refractive surgeons in solving some problems such as corneal topography screening and surgical design. From assisting in the diagnosis to the treatment options, artificial intelligence will play an increasingly important role in improving the safety, accuracy and efficiency of the surgery. AI may be a path forward to achieve customized and precise vision correction.

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References

  1. Kim TI, Alio Del Barrio JL, Wilkins M, Cochener B, Ang M. Refractive surgery. Lancet. 2019;393(10185):2085–98. https://doi.org/10.1016/S0140-6736(18)33209-4.

    Article  PubMed  Google Scholar 

  2. Lin SR, Ladas JG, Bahadur GG, Al-Hashimi S, Pineda R. A review of machine learning techniques for keratoconus detection and refractive surgery screening. Semin Ophthalmol. 2019;34(4):317–26. https://doi.org/10.1080/08820538.2019.1620812.

    Article  PubMed  Google Scholar 

  3. Ruiz Hidalgo I, Rodriguez P, Rozema JJ, Ni Dhubhghaill S, Zakaria N, Tassignon MJ, et al. Evaluation of a machine-learning classifier for keratoconus detection based on Scheimpflug tomography. Cornea. 2016;35(6):827–32. https://doi.org/10.1097/ICO.0000000000000834.

    Article  PubMed  Google Scholar 

  4. Mahmoud AM, Roberts C, Lembach R, Herderick EE, McMahon TT, Clek SG. Simulation of machine-specific topographic indices for use across platforms. Optom Vis Sci. 2006;83(9):682–93. https://doi.org/10.1097/01.opx.0000232944.91587.02.

    Article  PubMed  Google Scholar 

  5. Machado AP, Lyra JM, Ambrósio R, Ribeiro G, LPN A, Xavier C, et al., editors. Comparing machine-learning classifiers in keratoconus diagnosis from ORA examinations. Berlin: Springer; 2011.

    Google Scholar 

  6. Ambrosio R Jr, Lopes BT, Faria-Correia F, Salomao MQ, Buhren J, Roberts CJ, et al. Integration of Scheimpflug-based corneal tomography and biomechanical assessments for enhancing ectasia detection. J Refract Surg. 2017;33(7):434–43. https://doi.org/10.3928/1081597X-20170426-02.

    Article  PubMed  Google Scholar 

  7. Ma R, Liu Y, Zhang L, Lei Y, Hou J, Shen Z, et al. Distribution and trends in corneal thickness parameters in a large population-based multicenter study of young Chinese adults. Invest Ophthalmol Vis Sci. 2018;59(8):3366–74. https://doi.org/10.1167/iovs.18-24332.

    Article  PubMed  Google Scholar 

  8. Zou HH, Xu JH, Zhang L, Ji SF, Wang Y. Assistant diagnose for subclinical keratoconus by artificial intelligence. Zhonghua Yan Ke Za Zhi. 2019;55(12):911–5. https://doi.org/10.3760/cma.j.issn.0412-4081.2019.12.008.

    Article  CAS  PubMed  Google Scholar 

  9. Lopes BT, Ramos IC, Salomao MQ, Guerra FP, Schallhorn SC, Schallhorn JM, et al. Enhanced tomographic assessment to detect corneal ectasia based on artificial intelligence. Am J Ophthalmol. 2018;195:223–32. https://doi.org/10.1016/j.ajo.2018.08.005.

    Article  PubMed  Google Scholar 

  10. Ruiz Hidalgo I, Rozema JJ, Saad A, Gatinel D, Rodriguez P, Zakaria N, et al. Validation of an objective keratoconus detection system implemented in a Scheimpflug Tomographer and comparison with other methods. Cornea. 2017;36(6):689–95. https://doi.org/10.1097/ICO.0000000000001194.

    Article  PubMed  Google Scholar 

  11. Souza MB, Medeiros FW, Souza DB, Garcia R, Alves MR. Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinations. Clinics (Sao Paulo). 2010;65(12):1223–8. https://doi.org/10.1590/s1807-59322010001200002.

    Article  Google Scholar 

  12. Lavric A, Valentin P. KeratoDetect: keratoconus detection algorithm using convolutional neural networks. Comput Intell Neurosci. 2019;2019:8162567. https://doi.org/10.1155/2019/8162567.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Jin HY, Wan T, Wu F, Yao K. Comparison of visual results and higher-order aberrations after small incision lenticule extraction (SMILE): high myopia vs. mild to moderate myopia. BMC Ophthalmol. 2017;17(1):118. https://doi.org/10.1186/s12886-017-0507-2.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zhang J, Wang Y, Wu W, Xu L, Li X, Dou R. Vector analysis of low to moderate astigmatism with small incision lenticule extraction (SMILE): results of a 1-year follow-up. BMC Ophthalmol. 2015;15:8. https://doi.org/10.1186/1471-2415-15-8.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Shapira Y, Vainer I, Mimouni M, Sela T, Munzer G, Kaiserman I. Myopia and myopic astigmatism photorefractive keratectomy: applying an advanced multiple regression-derived nomogram. Graefes Arch Clin Exp Ophthalmol. 2019;257(1):225–32. https://doi.org/10.1007/s00417-018-4101-y.

    Article  PubMed  Google Scholar 

  16. Moniz N, Fernandes ST. Nomogram for treatment of astigmatism with laser in situ keratomileusis. J Refract Surg. 2002;18(3 Suppl):S323–6.

    PubMed  Google Scholar 

  17. Liyanage SE, Allan BD. Multiple regression analysis in myopic wavefront laser in situ keratomileusis nomogram development. J Cataract Refract Surg. 2012;38(7):1232–9. https://doi.org/10.1016/j.jcrs.2012.02.043.

    Article  PubMed  Google Scholar 

  18. Seider MI, McLeod SD, Porco TC, Schallhorn SC. The effect of procedure room temperature and humidity on LASIK outcomes. Ophthalmology. 2013;120(11):2204–8. https://doi.org/10.1016/j.ophtha.2013.04.015.

    Article  PubMed  Google Scholar 

  19. Neuhaus-Richard I, Frings A, Ament F, Görsch IC, Druchkiv V, Katz T, et al. Do air pressure and wind speed influence the outcome of myopic laser refractive surgery? Results from the Hamburg weather study. Int Ophthalmol. 2014;34(6):1249–58. https://doi.org/10.1007/s10792-014-9923-y.

    Article  PubMed  Google Scholar 

  20. Cui T, Wang Y, Ji S, Li Y, Hao W, Zou H, et al. Applying machine learning techniques in nomogram prediction and analysis for SMILE treatment. Am J Ophthalmol. 2020;210:71–7. https://doi.org/10.1016/j.ajo.2019.10.015.

    Article  PubMed  Google Scholar 

  21. Sanders DR, Doney K, Poco M. United States Food and Drug Administration clinical trial of the Implantable Collamer Lens (ICL) for moderate to high myopia: three-year follow-up. Ophthalmology J. 2004;111(9):1683–92. https://doi.org/10.1016/j.ophtha.2004.03.026.

    Article  Google Scholar 

  22. Zadnik K, Money S, Lindsley K. Intrastromal corneal ring segments for treating keratoconus. Cochrane Database Syst Rev. 2019;5 https://doi.org/10.1002/14651858.CD011150.pub2.

  23. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88. https://doi.org/10.1016/j.media.2017.07.005.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Tianjin Eye Hospital, Tianjin Eye Institute, Tianjin Key Laboratory of Ophthalmology and Visual Science, Nankai University School of Medicine, Nankai University, Tianjin, China

    Yan Wang, Mohammad Alzogool & Haohan Zou

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

    Yan Wang & Haohan Zou

Authors
  1. Yan Wang
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  2. Mohammad Alzogool
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  3. Haohan Zou
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Editors and Affiliations

  1. Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland

    Andrzej Grzybowski

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Wang, Y., Alzogool, M., Zou, H. (2021). Artificial Intelligence in Refractive Surgery. In: Grzybowski, A. (eds) Artificial Intelligence in Ophthalmology. Springer, Cham. https://doi.org/10.1007/978-3-030-78601-4_17

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  • DOI: https://doi.org/10.1007/978-3-030-78601-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78600-7

  • Online ISBN: 978-3-030-78601-4

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