Thales Levi Azevedo Valente1, João Dallyson Sousa de Almeida2, Aristófanes Corrêa Silva3, Jorge Antonio Meireles Teixeira4, Marcelo Gattass5. 1. Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil. Electronic address: selaht7@gmail.com. 2. Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil. Electronic address: joao.dallyson@ufma.br. 3. Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil. Electronic address: ari@dee.ufma.br. 4. Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil. Electronic address: jorgemeireles1@gmail.com. 5. Pontifical Catholic University of Rio de Janeiro - PUC-Rio, R. São Vicente, 225, Gávea, 22453-900, Rio de Janeiro, RJ, Brazil. Electronic address: mgattass@tecgraf.puc-rio.br.
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 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.
BACKGROUND AND OBJECTIVE: Medical image processing can contribute to the detection and diagnosis of humanbody 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.
Authors: S Tengtrisorn; A Tungsattayathitthan; S Na Phatthalung; P Singha; N Rattanalert; S Bhurachokviwat; S Chouyjan Journal: PLoS One Date: 2021-12-01 Impact factor: 3.240