Day to day clinically relevant corneal elevation, thickness, and curvature parameters using the orbscan II scanning slit topographer and the pentacam scheimpflug imaging device

Hashemi, Hassan; Mehravaran, Shiva

doi:10.4103/0974-9233.61216

Cited by 27 publications

(2 citation statements)

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“…Modern Placido disc-based corneal topography devices are still popular, as they provide information about corneal curvature, surface irregularities, and aberrations by generating color-coded maps ( 1 , 8 ). More recently, Scheimpflug-based corneal tomography imaging has allowed for greater analysis of the cornea by generating three-dimensional representations of the anterior segment to provide information about corneal thickness as well as the anterior and posterior cornea ( 7 , 9 ). Corneal tomography is now the gold standard method for corneal ectasia diagnosis, and the Pentacam® (OCULUS, Arlington, WA, USA) is one of the most common Scheimpflug-based corneal tomography devices ( 9 , 10 ).…”

Section: Introductionmentioning

confidence: 99%

“…More recently, Scheimpflug-based corneal tomography imaging has allowed for greater analysis of the cornea by generating three-dimensional representations of the anterior segment to provide information about corneal thickness as well as the anterior and posterior cornea ( 7 , 9 ). Corneal tomography is now the gold standard method for corneal ectasia diagnosis, and the Pentacam® (OCULUS, Arlington, WA, USA) is one of the most common Scheimpflug-based corneal tomography devices ( 9 , 10 ). Another adjunct imaging technology used for evaluation of corneal ectasias is anterior-segment optical coherence tomography (AS-OCT), which generates cross-sectional corneal images that can be used to identify asymmetry in corneal thinning, posterior curvature, and epithelial/total corneal thickness via epithelial mapping ( 1 ).…”

Section: Introductionmentioning

confidence: 99%

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Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Goodman,

Zhu

2024

Front. Ophthalmol.

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IntroductionThe application of artificial intelligence (AI) systems in ophthalmology is rapidly expanding. Early detection and management of keratoconus is important for preventing disease progression and the need for corneal transplant. We review studies regarding the utility of AI in the diagnosis and management of keratoconus and other corneal ectasias.MethodsWe conducted a systematic search for relevant original, English-language research studies in the PubMed, Web of Science, Embase, and Cochrane databases from inception to October 31, 2023, using a combination of the following keywords: artificial intelligence, deep learning, machine learning, keratoconus, and corneal ectasia. Case reports, literature reviews, conference proceedings, and editorials were excluded. We extracted the following data from each eligible study: type of AI, input used for training, output, ground truth or reference, dataset size, availability of algorithm/model, availability of dataset, and major study findings.ResultsNinety-three original research studies were included in this review, with the date of publication ranging from 1994 to 2023. The majority of studies were regarding the use of AI in detecting keratoconus or subclinical keratoconus (n=61). Among studies regarding keratoconus diagnosis, the most common inputs were corneal topography, Scheimpflug-based corneal tomography, and anterior segment-optical coherence tomography. This review also summarized 16 original research studies regarding AI-based assessment of severity and clinical features, 7 studies regarding the prediction of disease progression, and 6 studies regarding the characterization of treatment response. There were only three studies regarding the use of AI in identifying susceptibility genes involved in the etiology and pathogenesis of keratoconus.DiscussionAlgorithms trained on Scheimpflug-based tomography seem promising tools for the early diagnosis of keratoconus that can be particularly applied in low-resource communities. Future studies could investigate the application of AI models trained on multimodal patient information for staging keratoconus severity and tracking disease progression.

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