Purpose: To assess the diagnostic values of corneal epithelial and stromal thickness distribution characteristics in forme fruste keratoconus (FFKC) and subclinical keratoconus (KC). Methods: This cross-sectional study was conducted at VISSUM Innovation and Miguel Hernandez University, Alicante, Spain. Twenty-seven eyes (27 subjects) with FFKC, 50 eyes (50 subjects) with subclinical KC with a best spectacle corrected distance visual acuity ≥20/20 (Snellen) (grade zero KC according to the Red Temática de Investigación Cooperativa en Salud classification), and 66 control eyes (66 subjects) were included. Epithelial and stromal thicknesses and epithelium/stroma (E/S) thickness ratio at center, thinnest point, 5-, and 8-mm circles obtained from the MS-39 device (CSO, Firenze, Italy) were compared among the control, FFKC, and subclinical KC groups. Results: The FFKC group had thinner 8-mm superior-nasal epithelium and higher central E/S ratio compared with the control group (P < 0.05). In the subclinical KC group, the E/S ratios in the 5-mm temporal and superior zones were higher than those in the control group (P < 0.05). The FFKC and subclinical KC groups had thinner stroma compared with the control group (P < 0.05). A two-parameter formula correctly classified 94% of the eyes with subclinical KC and 98.5% of the normals, whereas another three-parameter model had 75% sensitivity and 94.3% specificity for discriminating FFKC from normals. Conclusions: This study identified different epithelial distributional and behavioral patterns in eyes with FFKC and subclinical KC. Eyes with FFKC seem to have increased central E/S ratio and asymmetric superior-nasal epithelial thinning, whereas keratometric and volumetric alterations seem to be more prominent in subclinical KC.

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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.

The validation of new methods for the diagnosis of incipient cases of Keratoconus (KC) with mild visual limitation is of great interest in the field of ophthalmology. During the asymmetric progression of the disease, the current diagnostic indexes do not record the geometric decompensation of the corneal curvature nor the variation of the spatial profile that occurs in singular points of the cornea. The purpose of this work is to determine the structural characterization of the asymmetry of the disease by using morpho-geometric parameters in KC eyes with mild visual limitation including using an analysis of a patient-specific virtual model with the aid of computer-aided design (CAD) tools. This comparative study included 80 eyes of patients classified as mild KC according to the degree of visual limitation and a control group of 122 eyes of normal patients. The metric with the highest area under the receiver operating characteristic (ROC) curve was the posterior apex deviation. The most prominent correlation was found between the anterior and posterior deviations of the thinnest point for the mild keratoconic cases. This new custom computational approach provides the clinician with a three-dimensional view of the corneal architecture when the visual loss starts to impair.

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.