Early Tomographic Changes in the Eyes of Patients With Keratoconus

Shajari, Mehdi; Jaffary, Irfan; Herrmann, Kim; Grunwald, Christine; Steinwender, Gernot; Mayer, Wolfgang J.; Kohnen, Thomas

doi:10.3928/1081597x-20180124-01

Cited by 37 publications

(22 citation statements)

References 25 publications

(11 reference statements)

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“…Hwang et al [7] proposed a first model based on the combination of five metrics (AUC: 0.86, Sensitivity: 83%, Specificity: 83%). Similar results have been obtained by other authors [5,6,20] in the model development phase and based on the exclusive metrics of the same Scheimpflug technology. In contrast, our study obtained higher performance values for model development (AUC: 0.95) and similar ones in the validation phase (AUC: 0.85), and was composed of metrics that are not exclusive to the same Scheimpflug technology.…”

Section: Discussionsupporting

confidence: 89%

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A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development

2020

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This work pursues two objectives: defining a new concept of risk probability associated with suffering early-stage keratoconus, classifying disease severity according to the RETICS (Thematic Network for Co-Operative Research in Health) scale. It recruited 169 individuals, 62 healthy and 107 keratoconus diseased, grouped according to the RETICS classification: 44 grade I; 18 grade II; 15 grade III; 15 grade IV; 15 grade V. Different demographic, optical, pachymetric and eometrical parameters were measured. The collected data were used for training two machine-learning models: a multivariate logistic regression model for early keratoconus detection and an ordinal logistic regression model for RETICS grade assessments. The early keratoconus detection model showed very good sensitivity, specificity and area under ROC curve, with around 95% for training and 85% for validation. The variables that made the most significant contributions were gender, coma-like, central thickness, high-order aberrations and temporal thickness. The RETICS grade assessment also showed high-performance figures, albeit lower, with a global accuracy of 0.698 and a 95% confidence interval of 0.623–0.766. The most significant variables were CDVA, central thickness and temporal thickness. The developed web application allows the fast, objective and quantitative assessment of keratoconus in early diagnosis and RETICS grading terms.

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Section: Discussionsupporting

confidence: 89%

“…Several studies have reported similar results to ours about models based on Scheimpflug metrics [5][6][7]20]. Hwang et al [7] proposed a first model based on the combination of five metrics (AUC: 0.86, Sensitivity: 83%, Specificity: 83%).…”

Section: Discussionsupporting

confidence: 79%

A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development

2020

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“…Out of all deviation from normality indices, final D showed the best repeatability (RR = 12.91%) as well as excellent reliability (ICC = 0.994). While total D has been recommended as a valuable parameter in the detection 14,15 and evaluation of ectatic progression in previous studies, especially in early stages of keratoconus 16 , in this study, while performing quite well, it did overall rank lower than other measured parameters in terms of accuracy in repeated exams.…”

Section: Discussionmentioning

confidence: 51%

Comparison of variables measured with a Scheimpflug device for evaluation of progression and detection of keratoconus

Neuhann

Schuh

Krause

et al. 2020

Sci Rep

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Keratoconus is a progressive ectatic corneal disorder, which can result in severe visual impairment. The new ABCD keratoconus classification system allows differentiated description of the disease. Aim of the study was to evaluate the components of this novel staging system (ARC, PRC, thinnest pachymetry) as well as topometric indices, deviation of normality indices, and other parameters in terms of repeatability and reliability. 317 eyes with keratoconus were examined twice with a Scheimpflug device (Pentacam, Oculus). Bland Altman analysis and intraclass correlations were carried out to evaluate the parameters repeatability and reliability. Apart from IHA (ICC = 0.730), all parameters showed excellent reliability (ICC > 0.900). ARC, PRC, thinnest pachymetry, Kmax, CKI, KI, Rmin, and Progression Avg were the best repeatable parameters with relative repeatability values < 2.5%. Other parameters performing well in terms of repeatability were IHD, ISV, IVA, and final D (RR < 13%). Regression analysis revealed consistently high repeatability along all stages of keratoconus for PRC, thinnest pachymetry, and CKI. All parameters of the ABCD staging system showed excellent reliability and repeatability, PRC and thinnest pachymetry even at all stages of keratoconus and can consequently be reliably used in the determination of keratoconus progression.

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“…They found that nearly all 27 of the eyes had a progression in the posterior cornea elevation whereas the keratometry and pachymetry did not necessarily change between the 2 measurement points. 4 It is possible that keratoconus eyes could have been erroneously labeled as the unaffected AKC eyes (D-index ¼ 1.56AE0.79) in the present study, 1 resulting in a more "advanced form" of subclinical keratoconus in the current study cohort. This could have reduced the impact of the posterior corneal elevation, which is particularly relevant for the diagnostic sensitivity in the early stage of the disease.…”

mentioning

confidence: 84%

“…Despite applying these quantitative cutoffs, some of the 94 very asymmetric ectasia with normal topography eyes were still regarded to have a suspicious curvature map. 3 A recent study by Shajari et al 4 performed sequential measurements in 27 fellow eyes of unilateral keratoconus, which have not yet shown any ectatic changes. The D-index changed from 0.91AE0.39 to 1.48AE0.45 between the 2 measurements separated by a mean duration of 360AE246 days.…”

mentioning

confidence: 99%

Re: Hwang et al.: Distinguishing highly asymmetric keratoconus eyes using combined Scheimpflug and spectral-domain OCT analysis (Ophthalmology. 2018;125:1862-1871)

Wan

Chan

2019

Ophthalmology

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Early Tomographic Changes in the Eyes of Patients With Keratoconus

Cited by 37 publications

References 25 publications

A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development

A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development

Comparison of variables measured with a Scheimpflug device for evaluation of progression and detection of keratoconus

Re: Hwang et al.: Distinguishing highly asymmetric keratoconus eyes using combined Scheimpflug and spectral-domain OCT analysis (Ophthalmology. 2018;125:1862-1871)

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