Keratoconus Screening Indices and Their Diagnostic Ability to Distinguish Normal From Ectatic Corneas

Shetty, Rohit; Rao, Harsha L.; Khamar, Pooja; Sainani, Kanchan; Vunnava, KrishnaPoojita; Jayadev, Chaitra; Kaweri, Luci

doi:10.1016/j.ajo.2017.06.031

Cited by 121 publications

(116 citation statements)

References 14 publications

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“…Although the diagnostic value of posterior surface variables from Scheimpflug-based cameras remains controversial [ 29 ], the importance of the variables extracted from the posterior elevation map of Scheimpflug-based cameras for screening is well recognized [ 18 , 29 – 31 ]. Some studies showed that some variables, such as BAD-D from Pentacam, exhibited good performance in discriminating subclinical KC eyes from normal eyes [ 32 ]; a possible reason is that BAD-D utilized a regression model combined with some variables in elevations of both anterior and posterior corneal surfaces, corneal thickness, location of the thinnest point, Kmax, pachymetric regression and Ambrosio relational thickness [ 33 ]. But Hwang et al implied BAD-D and similar individual metrics did not perform well enough to accurately distinguish subclinical KC eyes from a normal cohort [ 8 ].…”

Section: Discussionmentioning

confidence: 99%

Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

et al. 2020

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Purpose To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination of Scheimpflug camera images and ultra-high-resolution optical coherence tomography (UHR-OCT) imaging data. Methods A total of 121 eyes from 121 participants were classified by 2 cornea experts into 3 groups: normal (50 eyes), with keratoconus (38 eyes) or with subclinical keratoconus (33 eyes). All eyes were imaged with a Scheimpflug camera and UHR-OCT. Corneal morphological features were extracted from the imaging data. A neural network was used to train a model based on these features to distinguish the eyes with subclinical keratoconus from normal eyes. Fisher’s score was used to rank the differentiable power of each feature. The receiver operating characteristic (ROC) curves were calculated to obtain the area under the ROC curves (AUCs). Results The developed classification model used to combine all features from the Scheimpflug camera and UHR-OCT dramatically improved the differentiable power to discriminate between normal eyes and eyes with subclinical keratoconus (AUC = 0.93). The variation in the thickness profile within each individual in the corneal epithelium extracted from UHR-OCT imaging ranked the highest in differentiating eyes with subclinical keratoconus from normal eyes. Conclusion The automated classification system using machine learning based on the combination of Scheimpflug camera data and UHR-OCT imaging data showed excellent performance in discriminating eyes with subclinical keratoconus from normal eyes. The epithelial features extracted from the OCT images were the most valuable in the discrimination process. This classification system has the potential to improve the differentiable power of subclinical keratoconus and the efficiency of keratoconus screening.

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Section: Discussionmentioning

confidence: 99%

Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

et al. 2020

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“…27 A study conducted by Shetty et al concluded that although the screening indices from the Scheimpflug devices can effectively differentiate KC from normal, the cutoff values are still not adequate for differentiation between normal and sub-clinical cases. 28 Hence, the importance of individual evaluation of the tomography maps of each patient.…”

Section: Dovepressmentioning

confidence: 99%

<p>Prevalence of Subclinical Keratoconus Among Pediatric Egyptian Population with Astigmatism</p>

Sidky¹,

Hassanein²,

Eissa³

et al. 2020

OPTH

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Purpose: To report the prevalence of subclinical keratoconus (KC), and KC suspects, in pediatric Egyptian population with astigmatic error ≥2 diopters (D), with the use of CSO Sirius tomographer. Methods: A cross-sectional study that included all the children aged 6-18 years with an astigmatic error of ≥2 D who attended the pediatric ophthalmology clinic for routine checkups during the period between March 2017 and December 2017 was done. All subjects underwent comprehensive ophthalmic examination, and corneal imaging using CSO Sirius instrument, a pentacam with Scheimpflug technology. Results: There were 547 subjects enrolled in our study. Mean age was 11.34 ± 3.03 (range 6-18) years. There were 278 subjects (50.8%) between 6 and 12 years, and 269 subjects (49.2%) between 12 and 18 years. Males represented 48.4% and females represented 51.6% of patients. Among all the subjects, there were 26 (4.8%) KC patients, 24 (4.4%) KC suspects, and 497 (90.9%) were normal. Conclusion: Prevalence of subclinical KC among pediatric age group with astigmatism is relatively high, hence the importance of corneal tomography screening of children with astigmatic error ≥2 D.

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“…39 Shetty and colleagues found better predictive ability with a similar cutoff value of 0.45 (AUROC 0.858, 54% sensitivity, 81.4% specificity). 40 The differences in predictive power between studies may be due in part to differing classifications of description asymmetric (subclinical) disease, as the eyes in the study by Shetty included eyes with asymmetric bow tie and skewed radial axis patterns.…”

Section: Discussionmentioning

confidence: 99%

Distinguishing Highly Asymmetric Keratoconus Eyes Using Dual Scheimpflug/Placido Analysis

Golan

Piccinini

Hwang

et al. 2019

American Journal of Ophthalmology

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Purpose: To identify the best metrics or combination of metrics that provide the highest predictive power between normal eyes and the clinically unaffected eye of patients with highly asymmetric keratoconus using data from a Dual Scheimpflug/Placido device Design: Retrospective case-control study. Methods: Combined Dual Scheimpflug/Placido imaging was obtained from the Galilei G 4 device (Ziemer Ophthalmic Systems AG; Port, Switzerland) in 31 clinically unaffected eyes with highly asymmetric keratoconus and 178 eyes from 178 patients with bilaterally normal corneal examinations that underwent uneventful LASIK with at least one year follow-up. Receiver operating characteristic (ROC) curves were generated to determine area under the curve (AUC), sensitivity, and specificity for 87 metrics, and logistic regression modeling was utilized to determine optimal variable combinations. Results: No individual metric achieved an AUC greater than 0.79. A combined model consisting of 9 metrics yielded an AUC of 0.96, with 90.3% sensitivity and 92.6% specificity. Among those 9 metrics included, 5 related to corneal pachymetry, Opposite Sector Index (OSI) and Anterior Height BFS Z from the anterior surface, Asphericity and Asymmetry Index (AAI), Posterior Height BFS Z, and Posterior Height BFS X from the posterior surface. The strongest variable in the model was the thinnest point location on the horizontal (x) axis.

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Keratoconus Screening Indices and Their Diagnostic Ability to Distinguish Normal From Ectatic Corneas

Cited by 121 publications

References 14 publications

Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

<p>Prevalence of Subclinical Keratoconus Among Pediatric Egyptian Population with Astigmatism</p>

Distinguishing Highly Asymmetric Keratoconus Eyes Using Dual Scheimpflug/Placido Analysis

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