ArticleDer Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft2021
[Keratoconus detection and classification from parameters of the Corvis®ST : A study based on algorithms of machine learning].
Article in Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- A Continuous Severity Index for Keratoconus Diagnosis Based on Kolmogorov-Arnold Networks in a Chinese Population.Ophthalmology science · 2026Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Keratectasia severity staging and progression assessment based on the biomechanical E-staging.Eye and vision (London, England) · 2024Review
- Corneal biomechanics in early diagnosis of keratoconus using artificial intelligence.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Review
- [Corneal biomechanics before and after cross-linking in patients with keratoconus].Die Ophthalmologie · 2023Article
- Screening of sensitiveFrontiers in bioengineering and biotechnology · 2023Article
- Development of a classification system based on corneal biomechanical properties using artificial intelligence predicting keratoconus severity.Eye and vision (London, England) · 2021Article
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Abstract
BACKGROUND AND
objectiveIn the last decades increasingly more systems of artificial intelligence have been established in medicine, which identify diseases or pathologies or discriminate them from complimentary diseases. Up to now the Corvis®ST (Corneal Visualization Scheimpflug Technology, Corvis®ST, Oculus, Wetzlar, Germany) yielded a binary index for classifying keratoconus but did not enable staging. The purpose of this study was to develop a prediction model, which mimics the topographic keratoconus classification index (TKC) of the Pentacam high resolution (HR, Oculus) with measurement parameters extracted from the Corvis®ST. PATIENTS AND
methodsIn this study 60 measurements from normal subjects (TKC 0) and 379 eyes with keratoconus (TKC 1-4) were recruited. After measurement with the Pentacam HR (target parameter TKC) a measurement with the Corvis®ST device was performed. From this device 6 dynamic response parameters were extracted, which were included in the Corvis biomechanical index (CBI) provided by the Corvis®ST (ARTh, SP-A1, DA ratio 1 mm, DA ratio 2 mm, A1 velocity, max. deformation amplitude). In addition to the TKC as the target, the binarized TKC (1: TKC 1-4, 0: TKC 0) was modelled. The performance of the model was validated with accuracy as an indicator for correct classification made by the algorithm. Misclassifications in the modelling were penalized by the number of stages of deviation between the modelled and measured TKC values.
resultsA total of 24 different models of supervised machine learning from 6 different families were tested. For modelling of the TKC stages 0-4, the algorithm based on a support vector machine (SVM) with linear kernel showed the best performance with an accuracy of 65.1% correct classifications. For modelling of binarized TKC, a decision tree with a coarse resolution showed a superior performance with an accuracy of 95.2% correct classifications followed by the SVM with linear or quadratic kernel and a nearest neighborhood classifier with cubic kernel (94.5% each).
conclusionThis study aimed to show the principle of supervised machine learning applied to a set-up for the modelled classification of keratoconus staging. Preprocessed measurement data extracted from the Corvis®ST device were used to mimic the TKC provided by the Pentacam device with a series of different algorithms of machine learning.
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