Computer aided diagnosis for suspect keratoconus detection

Issarti, Ikram; Consejo, Alejandra; Jiménez‐García, Marta; Hershko, Sarah; Koppen, Carina; Rozema, J.

doi:10.1016/j.compbiomed.2019.04.024

Cited by 70 publications

(69 citation statements)

References 64 publications

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“…20,22 Some more recent approaches added more sophisticated analysis techniques based on artificial intelligence (AI) and machine learning algorithms to enhance the detection rate of the early cases. 15,[24][25][26] These AI-based techniques, although solely based on macroscopic parameters, tend to be more accurate than traditional methods; however, they require large datasets for their development.…”

Section: Discussionmentioning

confidence: 99%

Keratoconus Detection Based on a Single Scheimpflug Image

Consejo

Solarski

Karnowski

et al. 2020

Trans. Vis. Sci. Tech.

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To introduce a new approach for keratoconus detection based on corneal microstructure observed in vivo derived from a single Scheimpflug image. Methods: Scheimpflug single-image snapshots from 25 control subjects and 25 keratoconus eyes were analyzed; from each group, five subjects were randomly selected to provide out-of-sample data. Each corneal image was segmented, after which the stromal pixel intensities were statistically modeled with a Weibull distribution. Distribution estimated parameters α and β, characterizing corneal microstructure, were used in combination with a macrostructure parameter, central corneal thickness (CCT), for the detection of keratoconus. In addition, receiver operating characteristic curves were used to determine the sensitivity and specificity of each parameter for keratoconus detection. Results: The combination of CCT (sensitivity = 88%; specificity = 84%) with microscopic parameters extracted from statistical modeling of light intensity distribution, α (sensitivity = 76%; specificity = 76%) and β (sensitivity = 96%; specificity = 88%), from a single Scheimpflug image was found to be a successful tool to differentiate between keratoconus and control eyes with no misclassifications (sensitivity = 100%; specificity = 100%) with coefficients of variation up to 2.5%. Conclusions: The combination of microscopic and macroscopic corneal parameters extracted from a static Scheimpflug image is a promising, non-invasive tool to differentiate corneal diseases without the need to perform measurements based on induced deformation of the corneal structure. Translational Relevance: The proposed methodology has the potential to support clinicians in the detection of keratoconus, without compromising patient comfort.

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Section: Discussionmentioning

confidence: 99%

Keratoconus Detection Based on a Single Scheimpflug Image

Consejo

Solarski

Karnowski

et al. 2020

Trans. Vis. Sci. Tech.

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“…Dos Santos et al 21 reported that the custom neural network architecture could segment both healthy and keratoconus images with high accuracy, and that deep learning algorithms could be applied for OCT image segmentation in various clinical settings. Issarti et al 22 stated that computer aided diagnosis detected suspect keratoconus with an accuracy of 96.56% (sensitivity 97.78%, specificity 95.56%), suggesting that the algorithm is highly accurate and provides a stable screening platform to assist ophthalmologists with the early detection of keratoconus. Since the inclusion criteria, the category of the disease, and the sample size, were different among these studies, we cannot directly compare the sensitivity and the specificity outcomes between these previous and current studies.…”

Section: Discussionmentioning

confidence: 99%

“…In ophthalmology, it has been mainly applied in the diagnosis of retinal diseases4–6 and glaucoma 7–9. Until now, there have been several studies on the sensitivity and the specificity of keratoconus detection using machine learning 10–22. However, most previous studies have merely used either topographic numeric indices measured with a Placido disk-based corneal topographer, or tomographic numeric indices measured with a scanning slit tomographer and a rotating Scheimpflug camera, for machine learning in order to discriminate keratoconus from normal corneas.…”

Section: Introductionmentioning

confidence: 99%

Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study

Kamiya

Ayatsuka²,

Kato³

et al. 2019

BMJ Open

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ObjectiveTo evaluate the diagnostic accuracy of keratoconus using deep learning of the colour-coded maps measured with the swept-source anterior segment optical coherence tomography (AS-OCT).DesignA diagnostic accuracy study.SettingA single-centre study.ParticipantsA total of 304 keratoconic eyes (grade 1 (108 eyes), 2 (75 eyes), 3 (42 eyes) and 4 (79 eyes)) according to the Amsler-Krumeich classification, and 239 age-matched healthy eyes.Main outcome measuresThe diagnostic accuracy of keratoconus using deep learning of six colour-coded maps (anterior elevation, anterior curvature, posterior elevation, posterior curvature, total refractive power and pachymetry map).ResultsDeep learning of the arithmetical mean output data of these six maps showed an accuracy of 0.991 in discriminating between normal and keratoconic eyes. For single map analysis, posterior elevation map (0.993) showed the highest accuracy, followed by posterior curvature map (0.991), anterior elevation map (0.983), corneal pachymetry map (0.982), total refractive power map (0.978) and anterior curvature map (0.976), in discriminating between normal and keratoconic eyes. This deep learning also showed an accuracy of 0.874 in classifying the stage of the disease. Posterior curvature map (0.869) showed the highest accuracy, followed by corneal pachymetry map (0.845), anterior curvature map (0.836), total refractive power map (0.836), posterior elevation map (0.829) and anterior elevation map (0.820), in classifying the stage.ConclusionsDeep learning using the colour-coded maps obtained by the AS-OCT effectively discriminates keratoconus from normal corneas, and furthermore classifies the grade of the disease. It is suggested that this will become an aid for improving the diagnostic accuracy of keratoconus in daily practice.Clinical trial registration number000034587.

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“…Machine learning models have been applied to detect KC at different clinical stages with a number of these presented as specific to a particular tomographic or topographic imaging system [18][19][20][21][22][23][24][25][26][27] . The majority of these studies have used a single machine learning method such as regression analysis [28][29][30] , a tree-based method 25,[31][32][33] , ensemble method 34,35 , discriminant function analysis 24,36,37 , support vector machine 19,23,38 , or neural network 20,21,[39][40][41][42] . Parameters derived from a particular topographic or tomographic imaging system were collected in these studies, and established the machine learning models without selecting important parameter combinations 18,19,22,40,43 .…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus

Cao

Verspoor

Sahebjada

et al. 2020

Trans. Vis. Sci. Tech.

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Purpose: Keratoconus (KC) represents one of the leading causes of corneal transplantation worldwide. Detecting subclinical KC would lead to better management to avoid the need for corneal grafts, but the condition is clinically challenging to diagnose. We wished to compare eight commonly used machine learning algorithms using a range of parameter combinations by applying them to our KC dataset and build models to better differentiate subclinical KC from non-KC eyes. Methods: Oculus Pentacam was used to obtain corneal parameters on 49 subclinical KC and 39 control eyes, along with clinical and demographic parameters. Eight machine learning methods were applied to build models to differentiate subclinical KC from control eyes. Dominant algorithms were trained with all combinations of the considered parameters to select important parameter combinations. The performance of each model was evaluated and compared. Results: Using a total of eleven parameters, random forest, support vector machine and k-nearest neighbors had better performance in detecting subclinical KC. The highest area under the curve of 0.97 for detecting subclinical KC was achieved using five parameters by the random forest method. The highest sensitivity (0.94) and specificity (0.90) were obtained by the support vector machine and the k-nearest neighbor model, respectively. Conclusions: This study showed machine learning algorithms can be applied to identify subclinical KC using a minimal parameter set that are routinely collected during clinical eye examination. Translational Relevance: Machine learning algorithms can be built using routinely collected clinical parameters that will assist in the objective detection of subclinical KC.

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Computer aided diagnosis for suspect keratoconus detection

Cited by 70 publications

References 64 publications

Keratoconus Detection Based on a Single Scheimpflug Image

Keratoconus Detection Based on a Single Scheimpflug Image

Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study

Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus

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