Volume 12 Issue 5
Sep.  2021
Turn off MathJax
Article Contents
DI Yu, LI Ying. The Application and Research Progress of Artificial Intelligence in Corneal Related Diseases[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 761-767. doi: 10.12290/xhyxzz.2020-0098
Citation: DI Yu, LI Ying. The Application and Research Progress of Artificial Intelligence in Corneal Related Diseases[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 761-767. doi: 10.12290/xhyxzz.2020-0098

The Application and Research Progress of Artificial Intelligence in Corneal Related Diseases

doi: 10.12290/xhyxzz.2020-0098

Bethune·Dry eye Diagnosis and Treatment Research Project BJ-GY2021015J

More Information
  • Corresponding author: LI Ying  Tel: 86-10-69152733, E-mail: liyingpumch@126.com
  • Received Date: 2020-12-27
  • Accepted Date: 2021-01-11
  • Available Online: 2021-09-01
  • Publish Date: 2021-09-30
  • Artificial intelligence(AI)is the frontier of computer science. In recent years, AI has developed rapidly in many fields, and its research in ophthalmology is also increasing. The research of AI in corneal related diseases mainly includes the early diagnosis and grading of keratoconus, preoperative evaluation of corneal refractive surgery, prediction of surgical parameters, judgment of the classification and degree of infectious keratitis, evaluation of reintervention after corneal transplantation, auxiliary detection of corneal nerve endings in diabetic peripheral neuropathy, and screening of pterygium. Through the neural network, the support vector machine, and the decision tree, the sensitivity and specificity of the model can reach more than 90%. AI can provide objective clinical decision-making for clinicians and precise clinical treatments for patients. This article reviews the research of AI in corneal diseases in recent years.
  • loading
  • [1] Rahimy E. Deep learning application in ophthalmology[J]. Curr Opin Ophthalmol, 2018, 29: 254-260. doi:  10.1097/ICU.0000000000000470
    [2] Lawrence DR, Palacios-González C, Harris J. Artificial intelligence[J]. Camb Q Healthc Ehics, 2016, 25: 250-261. doi:  10.1017/S0963180115000559
    [3] 陈有信, 张碧磊, 张弘哲. 眼科人工智能技术的现状与问题[J]. 中华眼底病杂志, 2019, 35: 119-123.

    Chen YX, Zhang BL, Zhang HZ. Insights and prospectives of ophthalmologic artificial intelligence technology[J]. Zhonghua Yandibing Zazhi, 2019, 35: 119-123.
    [4] Gulshan V, Peng L, Coram M, et al. Development and validation of deep learning algorithm for detection of retinopathy in retinal fundus photographs[J]. JAMA, 2016, 316: 2402-2410. doi:  10.1001/jama.2016.17216
    [5] Burlina PM, Joshi N, Pekala M, et al. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural network[J]. JAMA Ophthalmol, 2017, 135: 1170-1176. doi:  10.1001/jamaophthalmol.2017.3782
    [6] Wu X, Huang Y, Liu Z, et al. Universal artificial intelligence platform for collaborative management of cataracts[J]. Br J Ophthalmol, 2019, 103: 1553-1560. doi:  10.1136/bjophthalmol-2019-314729
    [7] Asaoka R, Murata H, Iwase A, et al. Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier[J]. Ophthalmology, 2016, 123: 1974-1980. doi:  10.1016/j.ophtha.2016.05.029
    [8] Wu XH, Liu L, Zhao L, et al. Application of artificial intelligence in anterior segment ophthalmic disease: diversity and standardization[J]. Ann Transl Med, 2020, 8: 714. doi:  10.21037/atm-20-976
    [9] Mahesh Kumar SV, Gunasundari R. Computer-aided diagnosis of anterior segment eye abnormalities using visible wavelength image analysis based machine learning[J]. J Med Syst, 2018, 42: 128. doi:  10.1007/s10916-018-0980-z
    [10] Long EP, Lin HT, Liu ZZ, et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts[J]. Nat Biomed Eng, 2017, 1: 0024. doi:  10.1038/s41551-016-0024
    [11] Fu H, Baskaran M, Xu Y, et al. A deep learning system for automated angle-closure detection in anterior segment optical coherence tomography images[J]. Am J Ophthalmol, 2019, 203: 37-45. doi:  10.1016/j.ajo.2019.02.028
    [12] Aloudat M, Faezipour M, El-Sayed A. High intraocular pressure detection from frontal eye images: a machine lean-ing based approach[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2018: 5406-5409. http://www.ncbi.nlm.nih.gov/pubmed/30441559
    [13] Godefrooij DA, de Wit GA, Uiterwaal CS, et al. Age-Specific incidence and prevalence of keratoconus: a nationwide registration study[J]. Am J Ophthalmol, 2017, 175: 169-172. doi:  10.1016/j.ajo.2016.12.015
    [14] de Sanctis U, Loiacono C, Richiardi L, et al. Sensitivity and specificity of posterior cornea elevation measure by Pentacam in discriminating keratoconus/subclinical keratoconus[J]. Ophthalmology, 2008, 115: 1534-1539. doi:  10.1016/j.ophtha.2008.02.020
    [15] Gordon-Shaag A, Millodot M, Ifrah R, et al. aberrations and tomography in normal, keratoconus-suspect, and keratoconic eyes[J]. Optom Vis Sci, 2012, 89: 411-418. doi:  10.1097/OPX.0b013e318249d727
    [16] Kuo BI, Chang WY, Liao TS, et al. Keratoconus screening based on deep learning approach of corneal topography[J]. Transl Vis Sci Technol, 2020, 25: 53. http://www.researchgate.net/publication/345453287_Keratoconus_Screening_Based_on_Deep_Learning_Approach_of_Corneal_Topography
    [17] Kamiya K, Ayatsuka Y, Kato Y, et al. Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study[J]. BMJ Open, 2019, 9: e021313. http://bmjopen.bmj.com/content/9/9/e031313.full
    [18] Lavric A, Valentin P. KeratoDetect: keratoconus detection algorithm using Convolutional neural networks[J]. Comput Intell Neurosci, 2019: 8162567. http://downloads.hindawi.com/journals/cin/2019/8162567.pdf
    [19] Issarti I, Consejo A, Jiménez-García M, et al. Computer aided diagnosis for suspect keratoconus detection[J]. Comput Biol Med, 2019, 109: 33-42. doi:  10.1016/j.compbiomed.2019.04.024
    [20] Yousefi S, Yousefi E, Takahashi H, et al. Keratoconus severity identification using unsupervised machine learning[J]. PLoS One, 2018, 13: e0205998. doi:  10.1371/journal.pone.0205998
    [21] Hidalgo IR, Rozema JJ, Saad A, et al. Validation of an objective keratoconus detection system implemented in a scheimpflug Tomographer and comparison with other methods[J]. Cornea, 2017, 36: 689-695. doi:  10.1097/ICO.0000000000001194
    [22] Hidalgo IR, Rodrigues P, Rozema JJ, et al. Evaluation of a Machine-Learning classifier for keratoconus detection based on scheimpflug tomography[J]. Cornea, 2016, 35: 827-832. doi:  10.1097/ICO.0000000000000834
    [23] Kovács I, Miháltz K, Kránitz K, et al. Accuracy of machine learning classifiers using bilateral data from a scheimpflug camera for identifying eyes with preclinical signs of keratoconus[J]. J Cataract Refract Surg, 2016, 42: 275-283. doi:  10.1016/j.jcrs.2015.09.020
    [24] Smadja D, Touboul D, Cohen A, et al. Detection of subclinical keratoconus using an automated decision tree classification[J]. Am J Ophthalmol, 2013, 156: 237-246. doi:  10.1016/j.ajo.2013.03.034
    [25] Arbelaez MC, Versaci F, Vestri G, et al. Use of a support vector machine for keratoconus and subclinical keratoconus detection by topographic and tomographic data[J]. Ophthalmology, 2012, 119: 2231-2238. doi:  10.1016/j.ophtha.2012.06.005
    [26] Souza MB, Medeiros FM, Souza DB, et al. Evaluation of machine learning classifiers in keratoconus detection from Orbscan Ⅱ examinations[J]. Clnics(Sap Paulo), 2010, 65: 1223-1228. http://www.onacademic.com/detail/journal_1000040504536110_7e67.html
    [27] Twa M, Parthasarathy S, Cynthia R, et al. Automated decision tree classification of corneal shape[J]. Optom Vis Sci, 2005, 82: 1038-1046. doi:  10.1097/01.opx.0000192350.01045.6f
    [28] Accardo PA, Pensiero S. Neural network-based system for early keratoconus detection from corneal topography[J]. J Biomed Inform, 2002, 35: 151-159. doi:  10.1016/S1532-0464(02)00513-0
    [29] Smolek MK, Klyce SD. Current keratoconus detection methods compared with a neural network approach[J]. Invest Ophthalmol Vis Sci, 1997, 38: 2290-2299. http://www.researchgate.net/profile/Stephen_Klyce/publication/13885016_Current_keratoconus_detection_methods_compared_with_a_neural_network_approach/links/53f33d3d0cf2dd48950c9d4e
    [30] 王雁, 李晶. 正确应对角膜屈光手术发展中的问题及挑战[J]. 中华眼科杂志, 2018, 54: 3-6.

    Wang Y, Li J. Problems and challenges in the development of corneal refractive surgery[J]. Zhonghua Yanke Zazhi, 2018, 54: 3-6.
    [31] Sayegh FN. Age and refractive in 46, 000 patients as a potential predictor of refractive stability after refractive surgery[J]. J Refract Surg, 2009, 25: 747-751. doi:  10.3928/1081597X-20090707-10
    [32] Saad A, Gatinel D. Combining Placido and corneal wavefront data for the detection of forme fruste keratoconus[J]. J Refract Surg, 2016, 32: 510-516. doi:  10.3928/1081597X-20160523-01
    [33] Lopes BT, Ramos IC, Salomão MQ, et al. Enhanced tomographic assessment to detect corneal ectasia based on artificial intelligence[J]. Am J Ophthalmol, 2018, 195: 223-232. doi:  10.1016/j.ajo.2018.08.005
    [34] Yoo TK, Ryu IH, Lee GY, et al. Adopting machine learn-ing to automatically identify candidate patients for corneal refractive surgery[J]. NPJ Digit Med, 2019, 2: 59. doi:  10.1038/s41746-019-0135-8
    [35] Cui T, Wang Y, Ji SF, et al. Applying Machine Learning Techniques in Prediction and Analysis for SMILE Treatment[J]. Am J Ophthalmol, 2019, 210: 71-77. http://www.sciencedirect.com/science/article/pii/S0002939419305082
    [36] Ung L, Bispo PJM, Shanbhag SS, et al. The persistent dilemma of microbial keratitis: global burden, diagnosis, and antimicrobial resistance[J]. Surv Ophthalmol, 2019, 64: 255-271. doi:  10.1016/j.survophthal.2018.12.003
    [37] Saini JS, Jain AK, Kumar S, et al. Neural network approach to classify infective keratitis[J]. Curr Eye Res, 2003, 27: 111-116. doi:  10.1076/ceyr.
    [38] Wu XL, Qiu QC, Liu Z, et al. Hyphae Detection in Fungal Keratitis Images With Adaptive Robust Binary Pattern[J]. IEEE Access, 2018, 6: 13449-13460. doi:  10.1109/ACCESS.2018.2808941
    [39] Liu Z, Cao YK, Li YJ, et al. Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network[J]. Comput Methods Programs Biomed, 2020, 187: 105019. doi:  10.1016/j.cmpb.2019.105019
    [40] 刁玉梅, 洪晶. 角膜后弹力层内皮移植术的研究进展[J]. 中华眼科杂志, 2015, 51: 544-547. doi:  10.3760/cma.j.issn.0412-4081.2015.07.018

    Diao YM, Hong J. Research advances of Descemet's membrane endothelial keratoplasty[J]. Zhonghua Yanke Zazhi, 2015, 51: 544-547. doi:  10.3760/cma.j.issn.0412-4081.2015.07.018
    [41] Treder M, Lauermann JL, Alnawaiseh M, et al. Using Deep Learning in Automated Detection of Graft Detachment in Descemet Membrane Endothelial Keratoplasty: A Pilot Study[J]. Cornea, 2019, 38: 157-161. doi:  10.1097/ICO.0000000000001776
    [42] Hayashi T, Hitoshi T, Masumoto H, et al. A Deep Learning Approach in Rebubbling After Descemet's Membrane Endothelial Keratoplasty[J]. Eye Contact Lens, 2020, 46: 121-126 doi:  10.1097/ICL.0000000000000634
    [43] Dabbah MA, Graham J, Petropoulos I, et al. Dual-Model Automatic Detection of Nerve-Fibres in Corneal Confocal Microscopy Images[J]. Med Image Comput Comput Assist Inter, 2010, 13: 300-307. http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=PMC3066470&blobtype=pdf
    [44] Chen X, Graham J, Dabbah MA, et al. An Automatic Tool for Quantification of Nerve Fibres in Corneal Confocal Microscopy Images[J]. IEEE Trans Biomed Eng, 2017, 64: 786-794. doi:  10.1109/TBME.2016.2573642
    [45] Li Q, Zhong Y, Zhang T, et al. Quantitative analysis of corneal nerve fibers in type 2 diabetics with and without diabetic peripheral neuropathy: Comparison of manual and automated assessments[J]. Diabetes Res Clin Pract, 2019, 151: 33-38. doi:  10.1016/j.diabres.2019.03.039
    [46] Lopez YP, Aguilera LR. Automatic classification of pterygium-non pterygium images using deep learning[M/OL]. (2019-09-28). [2020-12-27]. https://link.springer.com/chapter/10.1007%2F978-3-030-32040-9_40.
    [47] Zulkifley MA, Abdani SR, Zulkifley NH. Pterygium-Net: a deep learning approach to pterygium detection and localization[J]. Multimed Tools Appl, 2019, 78: 34563-34584. doi:  10.1007/s11042-019-08130-x
    [48] Wan Zaki WMD, Mat Daud M, Abdani SR, et al. Automated pterygium detection method of anterior segment photographed images[J]. Comput Methods Programs Biomed, 2018, 154: 71-78. doi:  10.1016/j.cmpb.2017.10.026
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索


    Article Metrics

    Article views (765) PDF downloads(66) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint