Neural network-based system for early keratoconus detection from corneal topography

AbstractSome automatic methods have been proposed to identify keratoconus from corneal maps; among these methods, neural networks have proved to be useful. However, the identification of the early cases of this ocular disease remains a problem from both a diagnostic and a screening point of view. Another problem is whether a keratoconus screening must be performed taking into account both eyes of the same subject or each eye separately; hitherto, neural networks have only been used in the second alternative. In order to examine the differences of the two screening alternatives in terms of discriminative capability, several combinations of the number of input, hidden and output nodes and of learning rates have been examined in this study. The best results have been achieved by using as input the parameters of both eyes of the same subject and as output the three categories of clinical classification (normal, keratoconus, other alterations) for each subject, a low number of neurons in the hidden layer (lower than 10) and a learning rate of 0.1. In this case a global sensitivity of 94.1% (with a keratoconus sensitivity of 100%) in the test set as well as a global specificity of 97.6% (98.6% for keratoconus alone) have been reached

Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinations

PURPOSE: To evaluate the performance of support vector machine, multi-layer perceptron and radial basis function neural network as auxiliary tools to identify keratoconus from Orbscan II maps. METHODS: A total of 318 maps were selected and classified into four categories: normal (n = 172), astigmatism (n = 89), keratoconus (n = 46) and photorefractive keratectomy (n = 11). For each map, 11 attributes were obtained or calculated from data provided by the Orbscan II. Ten-fold cross-validation was used to train and test the classifiers. Besides accuracy, sensitivity and specificity, receiver operating characteristic (ROC) curves for each classifier were generated, and the areas under the curves were calculated. RESULTS: The three selected classifiers provided a good performance, and there were no differences between their performances. The area under the ROC curve of the support vector machine, multi-layer perceptron and radial basis function neural network were significantly larger than those for all individual Orbscan II attributes evaluated (p<0.05). CONCLUSION: Overall, the results suggest that using a support vector machine, multi-layer perceptron classifiers and radial basis function neural network, these classifiers, trained on Orbscan II data, could represent useful techniques for keratoconus detection

Characterization of corneal structure in keratoconus

Producción CientíficaThe increasing volume of patients interested in refractive surgery and the new treatment options available for keratoconus have generated a higher interest in achieving a better characterization of this pathology. The ophthalmic devices for corneal analysis and diagnosis have experienced a rapid development during the past decade with the implementation of technologies such as the Placido-disk corneal topography and the introduction of others such as scanning-slit topography, Scheimpflug photography, and optical coherence tomography, which are able to accurately describe not only the geometry of the anterior corneal surface but also that of the posterior surface, as well as pachymetry and corneal volume. Specifically, anterior and posterior corneal elevation, corneal power, pachymetry maps, and corneal coma-like aberrometry data provide sufficient information for an accurate characterization of the cornea to avoid misleading diagnoses of patients and provide appropriate counseling of refractive surgery candidates

Screening subclinical keratoconus with Placido-based corneal indices

Purpose: To assess in a sample of normal, keratoconic and keratoconus suspect eyes the performance of a set of new topographic indices computed directly from the digitized images of the Placido rings. Methods: This comparative study comprised a total of 124 eyes of 106 patients from the ophthalmic clinics Vissum Alicante and Vissum Almería (Spain), in three groups: control group (50 eyes), keratoconus group (50 eyes) and keratoconus suspect group (24 eyes). In all cases, a comprehensive examination was performed including the corneal topography with a Placido-based CSO topography system. Clinical outcomes were compared among groups, along with the discriminating performance of the proposed irregularity indices. Results: Significant differences at level 0.05 were found on the values of the indices among groups by means of Mann-Witney-Wilcoxon non-parametric test and Fisher’s exact test. Additional statistical methods, such as receiver operating characteristic analysis and K- fold cross-validation, confirmed the capability of the indices to discriminate between the three groups. Conclusions: Direct analysis of the digitized images of the Placido mires projected on the cornea is a valid and effective tool for detection of corneal irregularities. Although based only on the data from the anterior surface of the cornea, the new indices performed well even when applied to the keratoconus suspect eyes. They have the advantage of simplicity of calculation combined with high sensitivity in corneal irregularity detection, and thus can be used as supplementary criteria for diagnosing and grading keratoconus that can be added to the current keratometric classifications. Keywords: Corneal irregularities; subclinical keratoconus; irregularity index; diagnosis; corneal topography; Placido disk

Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps

The detection of keratoconus has been a difficult and arduous process over the years for ophthalmologists who have devised traditional approaches of diagnosis including the slit-lamp examination and observation of thinning of the corneal. The main contribution of this paper is using deep learning models namely Resnet50 and EfficientNet to not just detect whether an eye has been infected with keratoconus or not but also accurately detect the stages of infection namely mild, moderate, and advanced. The dataset used consists of corneal topographic maps and pentacam images. Individually the models achieved 97% and 94% accuracy on the dataset. We have also employed class activated maps (CAM) to observe and help visualize which areas of the images are utilized when making classifications for the different stages of keratoconus. Using deep learning models to predict the detection and severity of the infection can drastically speed up and provide accurate results at the same time

Protocol for the diagnosis of keratoconus using convolutional neural networks

Keratoconus is the corneal disease with the highest reported incidence of 1:2000. The treatment’s level of success highly depends on how early it was started. Subsequently, a fast and highly capable diagnostic tool is crucial. While there are many computer-based systems that are capable of the analysis of medical image data, they only provide parameters. These have advanced quite far, though full diagnosis does not exist. Machine learning has provided the capabilities for the parameters, and numerous similar scientific fields have developed full image diagnosis based on neural networks. The Homburg Keratoconus Center has been gathering almost 2000 patient datasets, over 1000 of them over the course of their disease. Backed by this databank, this work aims to develop a convolutional neural network to tackle diagnosis of keratoconus as the major corneal disease

A Mobile Solution for Lateral Segment Photographed Images based Deep Keratoconus Screening Method

Keratoconus (KC) is a condition of the bulging of the eye cornea. It is a common non-inflammatory ocular disorder that affects mostly the younger populace below the age of 30. &nbsp;The eye cornea bulges because of the conical displacement of either outwards or downwards. Such condition can greatly reduce one’s visual ability. Therefore, in this paper, we afford a mobile solution to mitigate the KC disorder using the state-of-the-art deep transfer learning method. We intend to use the pre-trained VGGNet-16 model and a conventional convolutional neural network to detect KC automatically. The experimental work uses a total of 4000 side view lateral segment photographed images (LSPIs) comprising 2000 of KC and non-KC or healthy each involving 125 subjects. The LSPIs were extracted from the video data captured using a smartphone. Fine tuning of three hyperparameters namely the learning rate (LR), batch size (BS) and epoch number (EN) were carried out during the training phase to generate the best model of which, the VGGNet-16 model fulfilled it. For the KC detection task, our proposed model achieves an accuracy of 95.75%, a sensitivity of 92.25%, and specificity of 99.25% using the LR, BS and EN of 0.0001, 16, and 70, respectively. These results confirmed the high potential of our proposed solution to apprehend KC prevalence towards an automated KC screening procedure

An Innovative Approach Based on Machine Learning to Evaluate the Risk Factors Importance in Diagnosing Keratoconus

Background and objective: Keratoconus is a non-inflammatory corneal condition affecting both eyes and is present in one out of every 2,000 people worldwide. The cornea deforms into a conical shape and thins, resulting in high-order aberrations and gradual vision loss. Risk factor analysis in the degradation of keratoconus is under-researched. Methods: This research work investigates and uses effective machine learning models to gain insight into how much the risk factors of a patient contribute towards the progressive stages of keratoconus, as well as how significant these factors are in the creation of an accurate prediction model. This research demonstrates the value of machine learning approaches on a clinical dataset. This research paper employs several machine learning algorithms to classify the patients' stage of keratoconus using clinical information, such as measurements of the cornea's topography, elevation, and pachymetry taken using pentacam equipment at Sydney's Vision Eye Institute Chatswood. Results: Eight different machine learning techniques were investigated over three variations of a dataset and achieved an average accuracy of 68, 80, and 90% for the risk factor, pentacam, and cumulative datasets, respectively. The results show a significant increase in accuracy and a 97% increase in AUC upon addition of risk factor data compared to the models trained on pentacam data alone. The machine learning methods shown in this paper outperform those in current research. Conclusions: This research highlights the importance of machine learning methods and risk factor data in the diagnosis of keratoconus and highlights the patient's primary optical aid as the strongest risk factor. The goal of this research is to support the work of the ophthalmologists in diagnosing keratoconus and provide better care for the patient