Laser refractive surgery has increased markedly in recent years, making the detection of corneal abnormalities extremely relevant. For this reason, an accurate diagnosis of clinical or subclinical keratoconus is critical. Corneal topography is the primary diagnostic tool for keratoconus detection, and pachymetry data and corneal aberrations are also commonly used. Recently, tomographic measurements using optical coherence tomography and corneal biomechanical indices have been used. In incipient and subclinical keratoconus, the use of a single parameter as a diagnostic factor is not sufficiently accurate. In these cases, the use of algorithms and predictive models is necessary. In addition, determining whether the disease will progress is crucial to selecting the most appropriate treatment. Some factors, such as age, keratometric indices, corneal elevation data, and corneal thickness, seem to be useful in predicting keratoconus progression.

New perspectives on the detection and progression of keratoconus.

Objective To evaluate the accuracy of convolutional neural networks technique (CNN) in detecting keratoconus using colour-coded corneal maps obtained by a Scheimpflug camera. Design Multicentre retrospective study. Methods and analysis We included the images of keratoconic and healthy volunteers’ eyes provided by three centres: Royal Liverpool University Hospital (Liverpool, UK), Sedaghat Eye Clinic (Mashhad, Iran) and The New Zealand National Eye Center (New Zealand). Corneal tomography scans were used to train and test CNN models, which included healthy controls. Keratoconic scans were classified according to the Amsler-Krumeich classification. Keratoconic scans from Iran were used as an independent testing set. Four maps were considered for each scan: axial map, anterior and posterior elevation map, and pachymetry map. Results A CNN model detected keratoconus versus health eyes with an accuracy of 0.9785 on the testing set, considering all four maps concatenated. Considering each map independently, the accuracy was 0.9283 for axial map, 0.9642 for thickness map, 0.9642 for the front elevation map and 0.9749 for the back elevation map. The accuracy of models in recognising between healthy controls and stage 1 was 0.90, between stages 1 and 2 was 0.9032, and between stages 2 and 3 was 0.8537 using the concatenated map. Conclusion CNN provides excellent detection performance for keratoconus and accurately grades different severities of disease using the colour-coded maps obtained by the Scheimpflug camera. CNN has the potential to be further developed, validated and adopted for screening and management of keratoconus.

https://bmjophth.bmj.com/content/bmjophth/6/1/e000824.full.pdf

Keratoconus detection of changes using deep learning of colour-coded maps

A combination of topographic and pachymetric parameters in keratoconus diagnosis.

http://rua.ua.es/dspace/bitstream/10045/106449/1/Velazquez_etal_2020_JAdvancedRes.pdf

Morphogeometric analysis for characterization of keratoconus considering the spatial localization and projection of apex and minimum corneal thickness point.

Purpose: To perform prediction analysis between topographic, pachymetric and wavefront parameters in keratoconus, suspects, and normal cases and to look at the possibility of a unified equation to evaluate keratoconus.Methods: This cross-sectional, observational study was done in cornea services of a specialty hospital. Fifty eyes of 50 candidates with a diagnosis of normal, keratoconus suspect, and keratoconus were included in each group (total 150 eyes). All eyes underwent detailed analysis on Scheimplug + Placido device (Sirius, CSO, Italy). Main parameters evaluated were topographic [maximum keratometry (Max Km), average keratometry and astigmatism at 3, 5, and 7 mm], pachymetric [central and minimum corneal thickness (MCT) and their difference, corneal volume] and corneal aberrations [higher order aberrations root-mean-square (HOARMS), coma, spherical, residual].Central tendency, predictive fits and regression models, were computed.Results: The measured variables had a significant difference ...

Predictive Analysis Between Topographic, Pachymetric and Wavefront Parameters in Keratoconus, Suspects and Normal Eyes: Creating Unified Equations to Evaluate Keratoconus

To analyze whether an association exists between keratometric and pachymetric changes in the cornea, and whether it can be used to create pachymetric cutoff criteria secondary to keratometric criteria. In this cross-sectional study, 1000 candidates presenting to the refractive surgery services of a tertiary care hospital underwent bilateral Orbscan IIz (Bausch and Lomb) assessment along with other ophthalmic evaluation. Stepwise regression analysis-based models showed that simulated keratometry (simK) astigmatism was significantly predicted by the minimum corneal thickness (MCT) and difference between central and MCT (δCT), mean SimK by the MCT and δCT, and maximum keratometry in the central 10-mm zone by the MCT and δCT (P 27 μm has only a 2.5% chance of being normal and not a keratoconus suspect or worse. Pachymetric diagnostic cutoffs can be used as adjuncts to the existing topographic criteria to screen keratoconus suspect and keratoconic eyes.

/pdf/a-new-pachymetry-based-approach-for-diagnostic-cutoffs-for-4y529nlaf8.pdf

A new, pachymetry-based approach for diagnostic cutoffs for normal, suspect and keratoconic cornea.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

P Desai

Author Tools

Chat about Author

Papers

A new, pachymetry-based approach for diagnostic cutoffs for normal, suspect and keratoconic cornea.