Computational method for aid in the diagnosis of sixth optic nerve palsy through digital videos

The sixth cranial nerve, also known as the abducens nerve, is responsible for controlling the movements of the lateral rectus muscle. Palsies on the sixth nerve prevent some muscles that control eye movements from proper functioning, causing headaches, migraines, blurred vision, vertigo, and double vision. Hence, such palsy should be diagnosed in the early stages to treat it without leaving any sequela. The usual methods for diagnosing the sixth nerve palsy are invasive or depend on expensive equipment, and computer-based methods designed specifically to diagnose the aforementioned palsy were not found until the publication of this work. Therefore, a low-cost, non-invasive method can support or guide the ophthalmologist's diagnosis. In this context, this work presents a computational methodology to aid in diagnosing the sixth nerve palsy using videos to assist ophthalmologists in the diagnostic process, serving as a second opinion. The proposed method uses convolutional neural networks and image processing techniques to track both eyes' movement trajectory during the video. With this trajectory, it is possible to calculate the average velocity (AV) in which each eye moves. Since it is known that paretic eyes move slower than healthy eyes, comparing the AV of both eyes can determine if the eye is healthy or paretic. The results obtained with the proposed method showed that paretic eyes move at least 19.65% slower than healthy ones. This threshold, along with the AV of the movement of the eyes, can help ophthalmologists in their analysis. The proposed method reached 92.64% accuracy in diagnosing the sixth optic nerve palsy (SONP), with a Kappa index of 0.925, which highlights the reliability of the results and gives favorable perspectives for further clinical application.

A mhealth application for automated detection and diagnosis of strabismus

BACKGROUND AND OBJECTIVE

Amblyopia is a public health problem, and strabismus is its primary cause. Our objective is to evaluate the concordance of the diagnosis of strabismus between strabismus expert ophthalmologist and the mhealth application developed for this purpose.

METHODS

We evaluated the concordance of the diagnosis of strabismus between the expert ophthalmologist and the mhealth application by screening 224 children and adolescents in the 5-15 years age group, with snapshots of patients' eyes and their analysis thereof. We were using a multifunctional cell phone with Android and the ophthalmologist's clinical evaluation by analyzing the ocular deviations using simple cover and prism and alternate cover.

RESULTS

Fraction measurements were used with two cutoff points of 6 and 11 prismatic diopters (PD). Results were compared according to their concordances, with a fair Kappa equal to 0.43 (95%CI = [0.38; 0.48]), which was statistically significant (p < 0.0001) at the cutoff point of 6 PD and Kappa equal to 0.49 (95% CI = [0.35; 0.61]), which was statistically significant (p < 0.042) in the cutoff point of 11 PD.

CONCLUSIONS

The cutoff point of 6 PD was chosen for screening by this mhealth application since it caused the loss of only two patients with strabismus, whereas, in the case of 11 PD, the loss was five patients in the universe of 224. These results are promising for the use of this software as a screening method for patients with strabismus.

Surgical planning of horizontal strabismus using multiple output regression tree

Strabismus is an eye disease that affects about 0.12%-9.86% of the population, which can cause irreversible sensory damage to vision and psychological problems. The most severe cases require surgical intervention, despite other less invasive techniques being available for a more conservative approach. As for surgeries, the treatment goal is to align the eyes to recover binocular vision, which demands knowledge, training, and experience. One of the leading causes of failure is human error during the measurement of deviation. Thus, this work proposes a new method based on the Decision Tree Regressor algorithms to assist in the surgical planning for horizontal strabismus to predict recoil and resection measures in the lateral and medial rectus muscles. In the presented method, two application approaches were taken, being in the form of multiple single target models, one procedure at a time, and the form of one multiple target model or all surgical procedures together. The method's efficiency is indicated by the average difference between the value indicated by the method and the physician's value. In our most accurate model, an average error of 0.66 mm was obtained for all surgical procedures, both for resection and recoil in the indication of the horizontal strabismus surgical planning. The results present the feasibility of using Decision Tree Regressor algorithms to perform the planning of strabismus surgeries, making it possible to predict correction values for surgical procedures based on medical data analysis and exceeding state-of-art.

Interferometer eye image classification for dry eye categorization using phylogenetic diversity indexes for texture analysis

Background and Objective Dry eye syndrome disease negatively impacts many people in various ways. Several tests are required to diagnose it for evaluating different physiological characteristics. One of the most applied tests for this is the manual classification of tear film images captured with Doane interferometer. Interferometry images can be categorized into five groups: debris, fine fringes, coalescing fine fringes, strong fringes, and coalescing strong fringes. Instability in the tear film creates the need for an automatic system to provide experts with diagnostic support. Therefore, the purpose of this study was to propose a method for automatic classification of the tear film lipid layer using phylogenetic diversity indexes for feature extraction and several classifiers. Methods The proposed method consisted of five main steps: (1) acquisition of VOPTICAL_GCU image dataset, (2) segmentation of the region of interest, (3) feature extraction using phylogenetic diversity indexes, (4) classification using the algorithms Support Vector Machines, Random Forest, Naive Bayes, Multilayer Perceptron, Random Tree, and RBFNetwork, and, (5) validation of results. Results The best result was obtained using Random Forest classifier, reaching an accuracy of over 97%, standard deviation of 0.51%, an area under the receiver operating characteristic curve of 0.99, a Kappa index of 0.96, and an F-Measure of 0.97. Conclusions The proposed method demonstrated that the tear film lipid layer classification problem can be resolved efficiently by using phylogenetic diversity indexes.

Automatic diagnosis of strabismus in digital videos through cover test

BACKGROUND AND OBJECTIVE

Medical image processing can contribute to the detection and diagnosis of human body anomalies, and it represents an important tool to assist in minimizing the degree of uncertainty of any diagnosis, while providing specialists with an additional source of diagnostic information. Strabismus is an anomaly that affects approximately 4% of the population. Strabismus modifies vision such that the eyes do not properly align, influencing binocular vision and depth perception. Additionally, it results in aesthetic problems, which can be reversed at any age. However, the use of low cost computational resources to assist in the diagnosis and treatment of strabismus is not yet widely available. This work presents a computational methodology to automatically diagnose strabismus through digital videos featuring a cover test using only a workstation computer to process these videos.

METHODS

The method proposed was validated in patients with exotropia and consists of eight steps: (1) acquisition, (2) detection of the region surrounding the eyes, (3) identification of the location of the pupil, (4) identification of the location of the limbus, (5) eye movement tracking, (6) detection of the occluder, (7) identification of evidence of the presence of strabismus, and (8) diagnosis.

RESULTS

To detect the presence of strabismus, the proposed method achieved a specificity value of 100%, and (2) a sensitivity value of 80%, with 93.33% accuracy in diagnosis of patients with extropia. This procedure was recognized to diagnose strabismus with an accuracy value of 87%, while acknowledging measures lower than 1Δ, and an average error in the deviation measure of 2.57Δ.

CONCLUSIONS

We demonstrated the feasibility of using computational resources based on image processing techniques to achieve success in diagnosing strabismus by using the cover test. Despite the promising results the proposed method must be validated in a greater volume of video including other types of strabismus.

Computer-Aided Methodology for Syndromic Strabismus Diagnosis

Strabismus is a pathology that affects approximately 4 % of the population, causing aesthetic problems reversible at any age and irreversible sensory alterations that modify the vision mechanism. The Hirschberg test is one type of examination for detecting this pathology. Computer-aided detection/diagnosis is being used with relative success to aid health professionals. Nevertheless, the routine use of high-tech devices for aiding ophthalmological diagnosis and therapy is not a reality within the subspecialty of strabismus. Thus, this work presents a methodology to aid in diagnosis of syndromic strabismus through digital imaging. Two hundred images belonging to 40 patients previously diagnosed by an specialist were tested. The method was demonstrated to be 88 % accurate in esotropias identification (ET), 100 % for exotropias (XT), 80.33 % for hypertropias (HT), and 83.33 % for hypotropias (HoT). The overall average error was 5.6Δ and 3.83Δ for horizontal and vertical deviations, respectively, against the measures presented by the specialist.

Computational methodology for automatic detection of strabismus in digital images through Hirschberg test

Strabismus is a pathology that affects about 4% of the population, causing aesthetic problems, reversible at any age; however, problems that can also cause irreversible muscular alterations, and alter the vision mechanism. The Hirschberg test is one of the exams used to detect this pathology. The application of high technology resources to help diagnose and treat ophthalmological conditions is, lamentably, not commonly found in the sub-specialty of strabismus. This work presents a methodology for automatic detection of strabismus in digital images through the Hirschberg test. For such, the work was organized into four stages: (1) finding the region of the eyes; (2) determining the precise location of the eyes; (3) locating the limbus and brightness; and (4) identifying strabismus. The methodology has produced results on the range of 100% sensibility, 91.3% specificity and 94% for the correct identification of strabismus, ensuring the efficiency of its geostatistical functions for the extraction of eye texture and for the calculation of the alignment between the eyes on digital images obtained from the Hirschberg test.