Prospects for early detection of keratoconus based on systems built for computer-assisted diagnostics of structural changes in the cornea

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The article is devoted to the problem of diagnosing subclinical keratoconus (KK). The need to identify early signs of KK is primarily associated with the potential for the development of iatrogenic keratoectasia in cases of underdiagnosis of the disease when determining the conditions for laser keratorefractive surgery involving a decrease in the thickness of the cornea. Today generally accepted algorithms for early computer-assisted diagnosis of KK are mainly based on the analysis of various morphometric parameters of the cornea, reflecting changes in its shape and thickness induced by structural abnormalities. Direct detection of structural changes in the cornea characteristic of early KK requires the use of high-tech imaging methods that are not always applicable in everyday clinical practice. The promising approach proposed in this study is based on the fact that a digital image of a corneal «slice» obtained using serial analyzers such as the Scheimpflug camera widely used in clinical practice provides indirect information about the structure of the epithelial layer, the local thickening of which takes place in the initial stages KK. It is this criterion that underlies the proposed system of computer-assisted diagnosis of KK. The carried out studies have shown the high sensitivity of this algorithm, and its specificity can be increased by involving the known diagnostic indicators of KK.

Keywords:

keratoconus

sheimpflug camera

corneal topography

optical coherence

corneal epithelium

Authors:

Avetisov S.E.

Krasnov Research Institute of Eye Disease;
I.M. Sechenov First Moscow State Medical University (Sechenov University)

ORCID: 0000-0001-7115-4275

Gridin V.N.

Center of Information Technologies in Design Russian Academy of Sciences

Bubnova I.A.

Research Institute of Eye Diseases

Lebedev A.S.

Center of Information Technologies in Design Russian Academy of Sciences

Novikov I.A.

Krasnov Research Institute of Eye Diseases

ORCID: 0000-0003-4898-4662

Received:

11.07.2021

Accepted:

06.10.2021

List of references:

Hashemi H, Heydarian S, Hooshmand E, Saatchi M, Yekta A, Aghamirsalim M, et al. The prevalence and risk factors for keratoconus: a systematic review and meta-analysis. Cornea. 2020;39(2):263-270. https://doi.org/10.1097/ICO.0000000000002150
Tur VM, MacGregor C, Jayaswal R, O’Brart D, Maycock N. A review of keratoconus: diagnosis, pathophysiology, and genetics. Surv Ophthalmol. 2017;62(6):770-783. https://doi.org/10.1016/j.survophthal.2017.06.009
Gordon-Shaag A, Millodot M, Shneor E, Liu Y. The genetic and environmental factors for keratoconus. BioMed Res Intern. 2015;2015. https://doi.org/10.1155/2015/795738
Avetisov SE, Novikov IA, Pateyuk LS. Keratoconus: etiological factors and accompanying manifestations. Vestnik oftal’mologii = The Russian Annals of Ophthalmology. 2014;130(4):110-116. (In Russ.).
Avetisov SE, Mamikonyan VR, Novikov IA, Pateyuk LS, Osipyan GA, Kiryushchenkova NP. Abnormal distribution of trace elements in keratoconic corneas. Vestnik oftal’mologii = The Russian Annals of Ophthalmology. 2015; 131(6):34-42. (In Russ.). https://doi.org/10.17116/oftalma2015131634-42
Kennedy RH, Bourne WM, Dyer JA. A 48-year clinical and epidemiologic study of keratoconus. Am J Ophthalmol. 1986;101(3):267-273. https://doi.org/10.1016/0002-9394(86)90817-2
Godefrooij DA, De Wit GA, Uiterwaal CS, Imhof SM, Wisse RP. Age-specific incidence and prevalence of keratoconus: a nationwide registration study. Am J Ophthalmol. 2017;175:169-172. https://doi.org/10.1016/j.ajo.2016.12.015
Reeves SW, Ellwein LB, Kim T, Constantine R, Lee PP. Keratoconus in the Medicare population. Cornea. 2009;28(1):40. https://doi.org/10.1097/ICO.0b013e3181839b06
Torres Netto EA, Al-Otaibi WM, Hafezi NL, Kling S, Al-Farhan HM, Randleman JB, Hafezi F. Prevalence of keratoconus in paediatric patients in Riyadh, Saudi Arabia. Br J Ophthalmol. 2018;102(10):1436-1441. Epub 2018 Jan 3. https://doi.org/10.1136/bjophthalmol-2017-311391
Millodot M, Shneor E, Albou S, Atlani E, Gordon-Shaag A. Prevalence and associated factors of keratoconus in Jerusalem: a cross-sectional study. Ophthalm Epidemiol. 2011;18(2):91-97. https://doi.org/10.3109/09286586.2011.560747
Barbara R, Gordon-Shaag A, Millodot M, Shneor E, Essa M, Anton M. Prevalence of keratoconus among young Arab students in Israel. Int J Keratoconus Ectatic Corneal Dis. 2014;3(1):9. https://doi.org/10.5005/jp-journals-10025-1070
Serdarogullari H, Tetikoglu M, Karahan H, Altin F, Elcioglu M. Prevalence of keratoconus and subclinical keratoconus in subjects with astigmatism using pentacam derived parameters. J Ophthalm Vision Res. 2013;8(3):213.
Belin MW, Villavicencio OF, Ambrósio Jr RR. Tomographic parameters for the detection of keratoconus: suggestions for screening and treatment parameters. Eye Contact Lens. 2014;40(6):326-330. https://doi.org/10.1097/ICL.0000000000000077
Corbett M, Maycock N, Rosen E, O’Brart D. Presentation of topographic information. Corneal Topography. Springer; 2019;51-82.
Demir S, Ortak H, Yeter V, Alim S, Sayn O, Tas U, et al. Mapping corneal thickness using dual-scheimpflug imaging at different stages of keratoconus. Cornea. 2013;32(11):1470-1474. https://doi.org/10.1097/ICO.0b013e3182a7387f
Bejdic N, Biscevic A, Pjano MA, Ivezic B. Incidence of Keratoconus in Refractive Surgery Population of Vojvodina-Single Center Study. Materia Socio-medica. 2020;32(1):46. https://doi.org/10.5455/msm.2020.32.46-49
Moshirfar M, Smedley JG, Muthappan V, Jarsted A, Ostler EM. Rate of ectasia and incidence of irregular topography in patients with unidentified preoperative risk factors undergoing femtosecond laser-assisted LASIK. Clin Ophthalmol. 2014;8:35. https://doi.org/10.2147/OPTH.S53370
Rubinfeld RS, Caruso C, Ostacolo C. Corneal cross-linking: the science beyond the myths and misconceptions. Cornea. 2019;38(6):780-790. https://doi.org/10.1097/ICO.0000000000001912
Gomes JA, Tan D, Rapuano CJ, Belin MW, Ambrósio Jr R, Guell JL, et al. Global consensus on keratoconus and ectatic diseases. Cornea. 2015;34(4): 359-369. https://doi.org/10.1097/ICO.0000000000000408
Kasparova EA. Modern ideas about the etiology and pathogenesis of keratoconus. Vestnik oftal’mologii = The Russian Annals of Ophthalmology. 2002; 118(3):50-53. (In Russ.).
Rabinowitz YS. Keratoconus. Surv Ophthalmol. 1998;42(4):297-319. https://doi.org/10.1016/S0039-6257(97)00119-7
Egorova GB, Rogova AYa. Keratoconus. Diagnostic and monitoring methods. Vestnik oftal’mologii = The Russian Annals of Ophthalmology. 2013;129(1): 61-66. (In Russ.).
Cao K, Verspoor K, Sahebjada S, Baird PN. Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus. Translat Vision Sci Technol. 2020;9(2):24. https://doi.org/10.1167/tvst.9.2.24
Lavric A, Popa V, Takahashi H, Yousefi S. Detecting Keratoconus From Corneal Imaging Data Using Machine Learning. IEEE Access. 2020;8:149113-149121. https://doi.org/10.1109/access.2020.3016060
Hidalgo IR, Rodriguez P, Rozema JJ, Dhubhghaill SN, Zakaria N, Tassignon M-J, et al. Evaluation of a machine-learning classifier for keratoconus detection based on Scheimpflug tomography. Cornea. 2016;35(6):827-832. https://doi.org/10.1097/ICO.0000000000000834
Kovács I, Miháltz K, Kránitz K, Juhász É, Takács Á, Dienes L, et al. Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus. J Cataract Refract Surg. 2016;42(2):275-283. https://doi.org/10.1016/j.jcrs.2015.09.020
Uçakhan ÖÖ, Çetinkor V, Özkan M, Kanpolat A. Evaluation of Scheimpflug imaging parameters in subclinical keratoconus, keratoconus, and normal eyes. J Cataract Refract Surg. 2011;37(6):1116-1124. https://doi.org/10.1016/j.jcrs.2010.12.049
Reinstein DZ, Archer TJ, Gobbe M. Corneal epithelial thickness profile in the diagnosis of keratoconus. J Refract Surg. 2009;25(7):604-610. https://doi.org/10.3928/1081597X-20090610-06
Reinstein DZ, Gobbe M, Archer TJ, Silverman RH, Coleman DJ. Epithelial, stromal, and total corneal thickness in keratoconus: three-dimensional display with artemis very-high frequency digital ultrasound. J Refract Surg. 2010;26(4):259-271. https://doi.org/10.3928/1081597X-20100218-01
Kanellopoulos AJ, Asimellis G. OCT corneal epithelial topographic asymmetry as a sensitive diagnostic tool for early and advancing keratoconus. Clin Ophthalmol. 2014;8:2277. https://doi.org/10.2147/OPTH.S67902
Gridin VN, Novikov IA, Solodovnikov VI, Trufanov MI, Lebedev AS, Bubnova IA, et al. Calculation error of local radius of anterior surface curvature of the cornea by optical cuts as independent diagnostic characteristic of keratokonus (preliminary report). Meditsina. 2019;7(1):42-54. (In Russ.). https://doi.org/10.29234/2308-9113-2019-7-1-42-54
Avetisov SE, Narbut MN. Corneal transparency: anatomical basis and evaluation methods. Vestnik oftal’mologii = The Russian Annals of Ophthalmology. 2017;133(5):84-91. https://doi.org/10.17116/oftalma2017133584-90
Gridin VN, Lebedev AS, Bubnova IA, Novikov IA, Tarasova OB, Salem BR. Digital image analysis of optical sections of the cornea for the diagnosis of early keratoconus. Informatsionnye tekhnologii i vychislitel’nye sistemy. 2020; (2):62-74 (In Russ.). https://doi.org/10.14357/20718632200206
Hashemi H, Khabazkhoob M, Pakzad R, Bakhshi S, Ostadimoghaddam H, Asaharlous A, et al. Pentacam accuracy in discriminating keratoconus from normal corneas: a diagnostic evaluation study. Eye Contact Lens. 2019;45(1): 46-50. https://doi.org/10.1097/ICL.0000000000000531
Shetty R, Rao H, Khamar P, Sainani K, Vunnava K, Jayadev C, et al. Keratoconus screening indices and their diagnostic ability to distinguish normal from ectatic corneas. Am J Ophthalmol. 2017;181:140-148. https://doi.org/10.1016/j.ajo.2017.06.031

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