Automatic diagnosis of strabismus in digital videos through cover test. (March 2017) - Valente, Thales Levi Azevedo; de Almeida, João Dallyson Sousa; Silva, Aristófanes Corrêa; Teixeira, Jorge Antonio Meireles; Gattass, Marcelo
Highlights: This work investigates computational method for automatic diagnose of strabismus. The strabismus was detected in digital video through the Cover Test. The method uses image and video processing and requires only a digital camera and a regular computer. The method achieved 87% of accuracy in diagnosing strabismus. The overall average error was lower than 1 Δ, and an average error of 2.6 Δ in deviation measure. Abstract: 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 1 Δ, and an average error in the deviation measure of 2.57 Δ . 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.