Smart decision support system for keratoconus severity staging using corneal curvature and thinnest pachymetry indices.

BACKGROUND: This study proposes a decision support system created in collaboration with machine learning experts and ophthalmologists for detecting keratoconus (KC) severity. The system employs an ensemble machine model and minimal corneal measurements. METHODS: A clinical dataset is initially obtained from Pentacam corneal tomography imaging devices, which undergoes pre-processing and addresses imbalanced sampling through the application of an oversampling technique for minority classes. Subsequently, a combination of statistical methods, visual analysis, and expert input is employed to identify Pentacam indices most correlated with severity class labels. These selected features are then utilized to develop and validate three distinct machine learning models. The model exhibiting the most effective classification performance is integrated into a real-world web-based application and deployed on a web application server. This deployment facilitates evaluation of the proposed system, incorporating new data and considering relevant human factors related to the user experience. RESULTS: The performance of the developed system is experimentally evaluated, and the results revealed an overall accuracy of 98.62%, precision of 98.70%, recall of 98.62%, F1-score of 98.66%, and F2-score of 98.64%. The application's deployment also demonstrated precise and smooth end-to-end functionality. CONCLUSION: The developed decision support system establishes a robust basis for subsequent assessment by ophthalmologists before potential deployment as a screening tool for keratoconus severity detection in a clinical setting.

Supervised classification of bradykinesia in Parkinson's disease from smartphone videos.

BACKGROUND: Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best. AIM: We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia. METHODS: We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0-1) or mild/moderate/severe bradykinesia (UPDRS = 2-4), and presence or absence of Parkinson's diagnosis. RESULTS: A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67. CONCLUSION: The method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.

Corneal Confocal Microscopy detects a Reduction in Corneal Endothelial Cells and Nerve Fibres in Patients with Acute Ischemic Stroke.

Endothelial dysfunction and damage underlie cerebrovascular disease and ischemic stroke. We undertook corneal confocal microscopy (CCM) to quantify corneal endothelial cell and nerve morphology in 146 patients with an acute ischemic stroke and 18 age-matched healthy control participants. Corneal endothelial cell density was lower (P < 0.001) and endothelial cell area (P < 0.001) and perimeter (P < 0.001) were higher, whilst corneal nerve fibre density (P < 0.001), corneal nerve branch density (P < 0.001) and corneal nerve fibre length (P = 0.001) were lower in patients with acute ischemic stroke compared to controls. Corneal endothelial cell density, cell area and cell perimeter correlated with corneal nerve fiber density (P = 0.033, P = 0.014, P = 0.011) and length (P = 0.017, P = 0.013, P = 0.008), respectively. Multiple linear regression analysis showed a significant independent association between corneal endothelial cell density, area and perimeter with acute ischemic stroke and triglycerides. CCM is a rapid non-invasive ophthalmic imaging technique, which could be used to identify patients at risk of acute ischemic stroke.

A fully automated cell segmentation and morphometric parameter system for quantifying corneal endothelial cell morphology.

BACKGROUND AND OBJECTIVE: Corneal endothelial cell abnormalities may be associated with a number of corneal and systemic diseases. Damage to the endothelial cells can significantly affect corneal transparency by altering hydration of the corneal stroma, which can lead to irreversible endothelial cell pathology requiring corneal transplantation. To date, quantitative analysis of endothelial cell abnormalities has been manually performed by ophthalmologists using time consuming and highly subjective semi-automatic tools, which require an operator interaction. We developed and applied a fully-automated and real-time system, termed the Corneal Endothelium Analysis System (CEAS) for the segmentation and computation of endothelial cells in images of the human cornea obtained by in vivo corneal confocal microscopy. METHODS: First, a Fast Fourier Transform (FFT) Band-pass filter is applied to reduce noise and enhance the image quality to make the cells more visible. Secondly, endothelial cell boundaries are detected using watershed transformations and Voronoi tessellations to accurately quantify the morphological parameters of the human corneal endothelial cells. The performance of the automated segmentation system was tested against manually traced ground-truth images based on a database consisting of 40 corneal confocal endothelial cell images in terms of segmentation accuracy and obtained clinical features. In addition, the robustness and efficiency of the proposed CEAS system were compared with manually obtained cell densities using a separate database of 40 images from controls (n = 11), obese subjects (n = 16) and patients with diabetes (n = 13). RESULTS: The Pearson correlation coefficient between automated and manual endothelial cell densities is 0.9 (p < 0.0001) and a Bland-Altman plot shows that 95% of the data are between the 2SD agreement lines. CONCLUSIONS: We demonstrate the effectiveness and robustness of the CEAS system, and the possibility of utilizing it in a real world clinical setting to enable rapid diagnosis and for patient follow-up, with an execution time of only 6 seconds per image.

A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images.

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the nerve structures can assist the early diagnosis of this disease. This paper proposes a robust, fast and fully automatic nerve segmentation and morphometric parameter quantification system for corneal confocal microscope images. The segmentation part consists of three main steps. First, a preprocessing step is applied to enhance the visibility of the nerves and remove noise using anisotropic diffusion filtering, specifically a Coherence filter followed by Gaussian filtering. Second, morphological operations are applied to remove unwanted objects in the input image such as epithelial cells and small nerve segments. Finally, an edge detection step is applied to detect all the nerves in the input image. In this step, an efficient algorithm for connecting discontinuous nerves is proposed. In the morphometric parameters quantification part, a number of features are extracted, including thickness, tortuosity and length of nerve, which may be used for the early diagnosis of diabetic polyneuropathy and when planning Laser-Assisted in situ Keratomileusis (LASIK) or Photorefractive keratectomy (PRK). The performance of the proposed segmentation system is evaluated against manually traced ground-truth images based on a database consisting of 498 corneal sub-basal nerve images (238 are normal and 260 are abnormal). In addition, the robustness and efficiency of the proposed system in extracting morphometric features with clinical utility was evaluated in 919 images taken from healthy subjects and diabetic patients with and without neuropathy. We demonstrate rapid (13 seconds/image), robust and effective automated corneal nerve quantification. The proposed system will be deployed as a useful clinical tool to support the expertise of ophthalmologists and save the clinician time in a busy clinical setting.

In vivo confocal microscopic corneal images in health and disease with an emphasis on extracting features and visual signatures for corneal diseases: a review study.

There is an evolution in the demands of modern ophthalmology from descriptive findings to assessment of cellular-level changes by using in vivo confocal microscopy. Confocal microscopy, by producing greyscale images, enables a microstructural insight into the in vivo cornea in both health and disease, including epithelial changes, stromal degenerative or dystrophic diseases, endothelial pathologies and corneal deposits and infections. Ophthalmologists use acquired confocal corneal images to identify health and disease states and then to diagnose which type of disease is affecting the cornea. This paper presents the main features of the healthy confocal corneal layers and reviews the most common corneal diseases. It identifies the visual signatures of each disease in the affected layer and extracts the main features of this disease in terms of intensity, certain regular shapes with both their size and diffusion, and some specific region of interest. These features will lead towards the development of a complete automatic corneal diagnostic system that predicts abnormalities in the confocal corneal data sets.

Preparation of 2D sequences of corneal images for 3D model building.

A confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, medical practioners can extract clinical information on the state of health of the patient's cornea. In this work we are addressing problems associated with capturing and processing these images including blurring, non-uniform illumination and noise, as well as the displacement of images laterally and in the anterior-posterior direction caused by subject movement. The latter may cause some of the captured images to be out of sequence in terms of depth. In this paper we introduce automated algorithms for classification, reordering, registration and segmentation to solve these problems. The successful implementation of these algorithms could open the door for another interesting development, which is the 3D modelling of these sequences.