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.

Diagnóstico de glaucoma em retinografias baseado em geoestatística / Glaucoma diagnosis in retinography based on geostatistics

O glaucoma é uma das doenças que mais causam cegueira em todo o mundo. O Conselho Brasileiro de Oftalmologia (CBO) estima que no Brasil existam 985 mil portadores de glaucoma com mais de 40 anos de idade. A utilização de sistemas CAD e CADx tem contribuído para aumentar as chances de detecção e diagnósticos corretos,auxiliando os especialistas na tomada de decisões sobre o tratamento do glaucoma. OBJETIVO: Apresentar um método para diagnóstico do glaucoma em retinografias utilizando o LBP para representar a região do disco ótico, funções geoestatísticas para descrever padrões e o MVS para classificar as imagens. MÉTODOS: Executado em 3 etapas: Representação da imagem (1), Extração de Características com geoestatística (2) e Classificação e Validação (3). RESULTADOS: Foram obtidos 88% de especificidade, 82% de sensibilidade e 84% de acurácia no diagnóstico do glaucoma. CONCLUSÃO: O método mostrou-se promissor como uma forma de auxílio ao diagnóstico de glaucoma.

Glaucoma is one of the diseases that more cause blindness worldwide. The Brazilian Council of Ophthalmology (CBO) estimates that in Brazil there are 985,000 people with glaucoma over 40 years old. The use of CAD and CADxsystems has contributed to increase the chances of detection and correct diagnoses, they provide, helping specialists inmaking decisions on glaucoma treatment. OBJECTIVE: To introduce a method for diagnosing glaucoma in fundus imageusing the LBP to represent the optic disk region, geostatistical functions to describe patterns and SVM to classify the images. METHODS: Run in 3 steps: Image representation (2), Feature extraction with geostatistic (3) and Classification and Validation (4). RESULTS: we obtained 88% specificity, 82% sensitivity and 84% accuracy in the diagnosis of glaucoma. CONCLUSION: The method has shown promise as a tool to aid the diagnosis of glaucoma.

Surgical planning for horizontal strabismus using Support Vector Regression.

Strabismus is a pathology which affects about 4% of the population, causing esthetic problems (reversible at any age) and irreversible sensory disorders, altering the vision mechanism. Many techniques can be applied to settle the muscular balance, thus eliminating strabismus. However, when the conservative treatment is not enough, the surgical treatment is adopted, applying recoils or resections to the ocular muscles affected. The factors involved in the surgical strategy in cases of strabismus are complex, demanding both theoretical knowledge and experience from the surgeon. So, the present work proposes a methodology based on Support Vector Regression to help the physician with decision related to horizontal strabismus surgeries. The efficiency of the method at the indication of the surgical plan was evaluated through the average difference between the values that it provided and the values indicated by the specialists. In the planning of medial rectus muscles surgeries, the average error was 0.5mm for recoil and 0.7 for resection. For lateral rectus muscles, the mean error was 0.6 for recoil and 0.8 for resection. The results are promising and prove the feasibility of the use of Support Vector Regression in the indication of strabismus surgeries.

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.