Distinguishing Highly Asymmetric Keratoconus Eyes Using Combined Scheimpflug and Spectral-Domain OCT Analysis

Hwang, Eric S.; Perez-Straziota, Claudia; Kim, Sang Woo; Santhiago, Marcony R.; Randleman, J. Bradley

doi:10.1016/j.ophtha.2018.06.020

Cited by 115 publications

(104 citation statements)

References 48 publications

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“…The classification was significantly better, when indices from both devices were used simultaneously . The logistic regression model produced a differential classification of asymmetric normal eyes since the model included thickness variables, had smaller sample sizes and lacked FFKC eyes compared to our study . However, a comparison of the Pentacam indices from the earlier and our study revealed similar magnitudes for the VAE‐NT eyes.…”

Section: Discussionmentioning

confidence: 41%

“…The logistic regression model produced a differential classification of asymmetric normal eyes since the model included thickness variables, had smaller sample sizes and lacked FFKC eyes compared to our study . However, a comparison of the Pentacam indices from the earlier and our study revealed similar magnitudes for the VAE‐NT eyes. Thus, the earlier study supported our conclusion that the VAE‐NT eyes could have the same features as normal eyes topographically.…”

Section: Discussionmentioning

confidence: 58%

“…A recent study used a combination of Scheimpflug tomography and OCT based epithelium thickness to classify a group of normal and VAE‐NT eyes . The classification was significantly better, when indices from both devices were used simultaneously . The logistic regression model produced a differential classification of asymmetric normal eyes since the model included thickness variables, had smaller sample sizes and lacked FFKC eyes compared to our study .…”

Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

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Bowman's topography for improved detection of early ectasia

Chandapura

Salomão

Ambrósio

et al. 2019

Journal of Biophotonics

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The aim of this study was to evaluate whether OCT topography of the Bowman's layer and artificial intelligence (AI) can result in better diagnosis of forme fruste (FFKC) and clinical keratoconus (KC). Normal (n = 221), FFKC (n = 72) and KC (n = 116) corneas were included. Some of the FFKC and KC patients had the fellow eye (VAE‐NT) with normal topography (n = 30). The Scheimpflug and OCT scans of the cornea were analyzed. The curvature and surface aberrations (ray tracing) of the anterior corneal surface [air‐epithelium (A‐E) interface in OCT] and epithelium‐Bowman's layer (E‐B) interface (in OCT only) were calculated. Four random forest models were constructed: (1) Scheimpflug only; (2) OCT A‐E only; (3) OCT E‐B only; (4) OCT A‐E and E‐B combined. For normal eyes, both Scheimpflug and OCT (A‐E and E‐B combined) performed equally in identifying these eyes (P = .23). However, OCT A‐E and E‐B showed that most VAE‐NT eyes were topographically similar to normal eyes and did not warrant a separate classification based on topography alone. For identifying FFKC eyes, OCT A‐E and E‐B combined performed significantly better than Scheimpflug (P = .006). For KC eyes, both Scheimpflug and OCT performed equally (P = 1.0). Thus, OCT Topography of Bowman's layer significantly improved the detection of FFKC eyes.

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Section: Discussionmentioning

confidence: 41%

Section: Discussionmentioning

confidence: 58%

Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

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Bowman's topography for improved detection of early ectasia

Chandapura

Salomão

Ambrósio

et al. 2019

Journal of Biophotonics

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“…AI has been applied to various adult ophthalmic diseases, including diabetic retinopathy [1,[74][75][76][77], AMD [78][79][80][81][82][83], sight-threatening retinal disease [2,[84][85][86][87][88][89], glaucoma [90][91][92], intraocular lens calculation [93], and keratoconus [94]. It has also been used for robotassisted repair of epiretinal membranes [95], retinal vessel segmentation [96][97][98][99], and systemic disease prediction from fundus images [100].…”

Section: Non-pediatric Applicationsmentioning

confidence: 99%

Artificial intelligence for pediatric ophthalmology

Reid

Eaton

2019

Current Opinion in Ophthalmology

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Purpose of reviewDespite the impressive results of recent artificial intelligence (AI) applications to general ophthalmology, comparatively less progress has been made toward solving problems in pediatric ophthalmology using similar techniques. This article discusses the unique needs of pediatric ophthalmology patients and how AI techniques can address these challenges, surveys recent applications of AI to pediatric ophthalmology, and discusses future directions in the field. Recent findingsThe most significant advances involve the automated detection of retinopathy of prematurity (ROP), yielding results that rival experts. Machine learning (ML) has also been successfully applied to the classification of pediatric cataracts, prediction of post-operative complications following cataract surgery, detection of strabismus and refractive error, prediction of future high myopia, and diagnosis of reading disability via eye tracking. In addition, ML techniques have been used for the study of visual development, vessel segmentation in pediatric fundus images, and ophthalmic image synthesis. SummaryAI applications could significantly benefit clinical care for pediatric ophthalmology patients by optimizing disease detection and grading, broadening access to care, furthering scientific discovery, and improving clinical efficiency. These methods need to match or surpass physician performance in clinical trials before deployment with patients. Due to widespread use of closed-access data sets and software implementations, it is difficult to directly compare the performance of these approaches, and reproducibility is poor. Open-access data sets and software implementations could alleviate these issues, and encourage further AI applications to pediatric ophthalmology.

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