Orbscan Topography in Primary Open-Angle Glaucoma

Purpose: To compare anterior and posterior corneal curvatures between eyes with primary open-angle glaucoma (POAG) and healthy eyes. Methods: This is a prospective, cross-sectional, observer-masked study. A total of 138 white subjects (one eye per patient) were consecutively recruited; 69 eyes had POAG (study group), and the other 69 comprised a group of healthy control eyes matched for age and central corneal pachymetry with the study ones. Exclusion criteria included any corneal or ocular inflammatory disease, previous ocular surgery, or treatment with carbonic anhydrase inhibitors. The same masked observer performed Goldmann applanation tonometry, ultrasound pachymetry, and Orbscan II topography in all cases. Central corneal thickness, intraocular pressure, and anterior and posterior topographic elevation maps were analyzed and compared between both groups. Results: Patients with POAG had greater forward shifting of the posterior corneal surface than that in healthy control eyes (p < 0.01). Significant differences in anterior corneal elevation between controls and POAG eyes were also found (p < 0.01). Conclusions: Primary open-angle glaucoma eyes have a higher elevation of the posterior corneal surface than that in central corneal thickness–matched nonglaucomatous eyes.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.