Journal of Optoelectronics · Laser, Volume. 33, Issue 11, 1201(2022)

Keratoconus model for auxiliary diagnosis based on MLP neural network

LIU Yan1, LIU Fenglian1, WU Jianwu2, LI Kangsheng1, and WANG Riwei3、*
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    References(12)

    [1] [1] RAISKUP F,THEURING A,PILLUNAT L E,et al.Corneal collagen crosslinking with ribof lavin and ultraviolet-A light in progressive keratoconus:ten-year results[J].Journal of Cataract & Refractive Surgery,2015,41(1):41-46.

    [2] [2] CHAN K,HERSH P S.Removal and repositioning of intracorneal ring segments:improving corneal topography and clinical outcomes in keratoconus and ectasia[J].Cornea,2017,36(2):244-248.

    [3] [3] VINCIGUERRA R,AZZOLINI C,ROBERTS C J,et al.Biomechanical characterization of subclinical keratoconus without topographic or tomographic Abnormalities[J].Journal of Refractive Surgery,2017,33(6):399-407.

    [5] [5] MCMONNIES C W.Assessing corneal hysteresis using the ocular response analyzer[J].Optometry & Vision Science:Official Publication of the American Academy of Optometry,2012,89(3):E343-E349.

    [7] [7] AMBROSIO R JR,LOPES B T,FARIA-CORREIA F,et al.Integration of Scheimpflug-based corneal tomography and biomechanical assessments for enhancing ectasia detection[J].Journal of Refractive Surgery,2017,33(7):434-443.

    [8] [8] VINCIGUERRA R,AMBROSIO R JR,ELSHEIKH A,et al.Detection of keratoconus with a new biomechanical index[J].Journal of Refractive Surgery,2016,32(12):803-810.

    [9] [9] ELHAM R,JAFARZADEHPUR E,HASHEMI H,et al.Keratoconus diagnosis using Corvis ST measured biomechanical parameters[J].Journal of Current Ophthalmology,2017,29(3):175-181.

    [10] [10] LANGENBUCHER A,HAFNER L,EPPIG T,et al.Keratoconus detection and classification from parameters of the Corvis ST:A study based on algorithms of machine learning[J].Der Ophthalmologe:Zeitschrift der Deutschen Ophthalmologischen Gesellschaft,2021,118(7):697-706.

    [11] [11] LAVRIC A,POPA V,TAKAHASHI H,et al.Detecting keratoconus from corneal imaging data using machine learning[J].IEEE Access,2020,8:149113-149121.

    [12] [12] HERBER R,PILLUNAT L E,RAISKUP F.Development of a classification system based on corneal biomechanical properties using artificial intelligence predicting keratoconus severity[J].Eye and Vision,2021,8(1):21-21.

    [13] [13] ZEBOULON P,DEBELLEMANIERE G,BOUVET M,et al.Corneal topography raw data classification using a convolutional neural network[J].American Journal of Ophthalmology,2020,219:33-39.

    [14] [14] HALLETT N,YI K,DICK J,et al.Deep learning based unsupervised and semi-supervised classification for keratoconus[C]//2020 International Joint Conference on Neural Networks (IJCNN),July 19-24,2020,Glasgow,UK.New York:IEEE,20030847.

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    LIU Yan, LIU Fenglian, WU Jianwu, LI Kangsheng, WANG Riwei. Keratoconus model for auxiliary diagnosis based on MLP neural network[J]. Journal of Optoelectronics · Laser, 2022, 33(11): 1201

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    Paper Information

    Received: Mar. 3, 2022

    Accepted: --

    Published Online: Oct. 9, 2024

    The Author Email: WANG Riwei (wangrw@wzu.edu.cn)

    DOI:10.16136/j.joel.2022.11.0128

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