To establish a new procedure for 3D geometric reconstruction of the human cornea to obtain a solid model that represents a personalized and in vivo morphology of both the anterior and posterior corneal surfaces. This model is later analyzed to obtain geometric variables enabling the characterization of the corneal geometry and establishing a new clinical diagnostic criterion in order to distinguish between healthy corneas and corneas with keratoconus. The method for the geometric reconstruction of the cornea consists of the following steps: capture and preprocessing of the spatial point clouds provided by the Sirius topographer that represent both anterior and posterior corneal surfaces, reconstruction of the corneal geometric surfaces and generation of the solid model. Later, geometric variables are extracted from the model obtained and statistically analyzed to detect deformations of the cornea. The variables that achieved the best results in the diagnosis of keratoconus were anterior corneal surface area (ROC area: 0.847, p<0.000, std. error: 0.038, 95% CI: 0.777 to 0.925), posterior corneal surface area (ROC area: 0.807, p<0.000, std. error: 0.042, 95% CI: 0,726 to 0,889), anterior apex deviation (ROC area: 0.735, p<0.000, std. error: 0.053, 95% CI: 0.630 to 0.840) and posterior apex deviation (ROC area: 0.891, p<0.000, std. error: 0.039, 95% CI: 0.8146 to 0.9672). Geometric modeling enables accurate characterization of the human cornea. Also, from a clinical point of view, the procedure described has established a new approach for the study of eye-related diseases.

To characterize corneal structural changes in keratoconus using a new morphogeometric approach and to evaluate its potential diagnostic ability. Comparative study including 464 eyes of 464 patients (age, 16 and 72 years) divided into two groups: control group (143 healthy eyes) and keratoconus group (321 keratoconus eyes). Topographic information (Sirius, CSO, Italy) was processed with SolidWorks v2012 and a solid model representing the geometry of each cornea was generated. The following parameters were defined: anterior (A_ant) and posterior (A_post) corneal surface areas, area of the cornea within the sagittal plane passing through the Z axis and the apex (A_apexant, A_apexpost) and minimum thickness points (A_mctant, A_mctpost) of the anterior and posterior corneal surfaces, and average distance from the Z axis to the apex (D_apexant, D_apexpost) and minimum thickness points (D_mctant, D_mctpost) of both corneal surfaces. Significant differences among control and keratoconus group were found in A_apexant, A_apexpost, A_mctant, A_mctpost, D_apexant, D_apexpost (all p<0.001), A_post (p = 0.014), and D_mctpost (p = 0.035). Significant correlations in keratoconus group were found between A_ant and A_post (r = 0.836), A_mctant and A_mctpost (r = 0.983), and D_mctant and D_mctpost (r = 0.954, all p0.05, R² Nagelkerke: 0.926). The overall percentage of cases correctly classified by the model was 97.30%. Our morphogeometric approach based on the analysis of the cornea as a solid is useful for the characterization and detection of keratoconus.

Purpose: Create a unique predictive model based on a set of demographic, optical, and geometric variables with two objectives: classifying keratoconus (KC) in its first clinical manifestation stages and establishing the probability of having correctly classified each case. Methods: We selected 178 eyes of 178 subjects (115 males; 64.6%; 63 females, 35.4%). Of these, 74 were healthy control subjects, and 104 suffered from KC according to the RETICS grading system (61 early KC, 43 mild KC). Only one eye from each patient was selected, and 27 different parameters were studied (demographic, clinical, pachymetric, and geometric). The data obtained were used in an ordinal logistic regression model programmed as a web application capable of using new patient data for real-time predictions. Results: EMKLAS, an early and mild KC classifier, showed good training performance figures, with 73% global accuracy and a 95% confidence interval of 65% to 79%. This classifier is particularly accurate when validated by an independent sample for the control (79%) and mild KC (80%) groups. The accuracy of the early KC group was remarkably lower (69%). The variables included in the model were age, gender, corrected distance visual acuity, 8-mm corneal diameter, and posterior minimum thickness point deviation. Conclusions: Our web application allows fast, objective, and quantitative assessment of early and mild KC in detection and classification terms and assists ophthalmology professionals in diagnosing this disease. Translational Relevance: No single gold standard exists for detecting and classifying preclinical KC, but the use of our web application and EMKLAS score may aid the decision-making process of doctors.

There are numerous tomographic indices for the detection of keratoconus risk. When the indexes based on corneal volume are analyzed, two problems are presented: on the one hand, they are not very sensitive to the detection of incipient cases of keratoconus because they are not locally defined in the primary developmental region of the structural abnormalities; and on the other hand, they do not register the geometric decompensation driven by the asymmetry present during the disease progression. This work performed a morphogeometric modeling of the cornea by the aid of CAD tools and using raw topographic data (Sirius system, CSO, Firenze). For this method, four singular points present on the corneal surfaces were located and the following parameters based on corneal volume were calculated: VOL_mct, defined by the points of minimal thickness; VOL_aap, defined by the anterior corneal apex, and VOL_pap, defined by the posterior corneal apex. The results demonstrate that a further reduction of corneal volume in keratoconus happens and significantly progresses along the disease severity level. The combination of optical and volumetric data, that collect the sensitivity of the asymmetry generated by the disease, allows an accurate detection of incipient cases and follow up of the disease progression.

The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility.

Applying a structural equations modelling methodology, the study analyses the relationships and effects of self-regulated learning (social interaction learning strategies and motivation) and first-year university experiences on permanence in the sophomore year. The participants are 239 first-year students in different Engineering degrees at a public university in south-eastern Spain. Two alternative structural models are evaluated, showing the superiority of the model where first-year university experiences completely mediate the effects of self-regulated learning on permanence. Motivation and social interaction learning strategies show direct effects on first-year university experiences, and first-year university experiences, in turn, show direct effects on permanence; additionally, both motivation and social interaction learning strategies have indirect effects on permanence via first-year university experiences. The noteworthy theoretical implications of the results are discussed, as well as the teaching methodology and support services provided to first-year university students.