Purpose: To compare the thickness of all retinal layers between a nondiabetic group and diabetic patients without diabetic retinopathy (DR). Methods: Cross-sectional study, in which all subjects underwent an ophthalmic examination including optical coherence tomography. After automatic retinal segmentation, each retinal layer thickness (eight separate layers and overall thickness) was calculated in all nine Early Treatment Diabetic Retinopathy Study (ETDRS) areas. The choroidal thickness (CT) also was measured at five locations. Generalized additive regression models were used to analyze the data. Results: A total of 175 patients were recruited, 50 nondiabetic subjects and 125 diabetic patients without DR, stratified into three groups according to diabetes duration: group I (10 years, n = 31). Overall, groups I and III of diabetic patients had a decrease in the photoreceptor layer (PR) thickness, when compared with the nondiabetic subjects in six ETDRS areas (P < 0.0007). Patients with more recent diagnosis (group I) had thinner PR than those with moderate duration (group II). Interestingly, patients with longer known disease (group III) had the thinnest PR values. There were no overall differences in the remaining retinal parameters. Conclusions: Retinal thickness profile is not linear throughout disease duration. Even in the absence of funduscopic disease, PR layer in diabetic patients seems to differ from nondiabetic subjects, thus suggesting that some form of neurodegeneration may take place before clinical signs of vascular problems arise.

Introduction: The purpose of this study was to measure and to compare macular choroidal thickness (CT) between patients with mild Alzheimer's disease (AD), patients without AD, and elderly patients.Methods: CT was measured manually in 13 locations at 500‐μm intervals of a horizontal and a vertical section from the fovea. Linear regression models were used to analyze the data.Results: Fifty patients with a diagnosis of mild AD (73.1 years), 152 patients without AD (71.03 years), and 50 elderly without AD (82.14 years) were included. In the AD patients, CT was significantly thinner in all 13 locations (P < .001—comparing with age‐match group), and comparing with the elderly group, a more pronounced difference was found in two locations temporal to the fovea.Discussion: Patients with AD showed a significant choroidal thinning even when compared with elderly subjects. The reduction of CT may aid in the diagnoses of AD, probably reflecting the importance of vascular factors in their pathogenesis.

Purpose: To compare choroidal thickness (CT) between diabetic patients without diabetic retinopathy and a nondiabetic group. To explore how CT relates to disease duration, mean arterial pressure, glycemia, glycosylated hemoglobin, intraocular pressure, and ocular pulse amplitude. Methods: Choroidal thickness was assessed using a spectral-domain optical coherence tomography and enhanced depth mode at 13 locations (subfoveal and 3 measurements 500 μm apart in 4 directions—nasal, temporal, superior, and inferior). Linear regression models were used. Results: One hundred seventy-five patients were recruited (125 diabetic patients without diabetic retinopathy and 50 nondiabetic patients). In diabetic patients, although without statistical significance, CT showed a trend to be thicker in all locations (6.16–24.27 μm). Choroidal thickness was negatively associated with age (P < 0.001) in both groups, but only in the diabetic group, it was positively associated to ocular pulse amplitude (with a mean increase between 8.5 μm and 11.6 μm for each millimeter of mercury increase in ocular pulse amplitude). Diabetic patients' CT seems to stabilize after 150 months of diabetes, increase with higher glycemia levels (>160 mg/dL) while showing no fluctuation with glycosylated hemoglobin and mean arterial pressure. Conclusion: There seems to be a thickening of the choroid in diabetic patients without diabetic retinopathy. Moreover, this tissue may be functionally different in diabetes, as the pattern of associations seems to differ between groups.

The new Construction 4.0 paradigm takes advantage of existing technologies. In this scope, the development and application of image-based methods for evaluating and monitoring the state of conservation of buildings has shown significant growth, including support for maintenance plans. Recently, powerful algorithms have been applied to automatically evaluate the state of conservation of buildings using deep learning frameworks, which are utilised as a black-box approach. The large amount of data required for training, the difficulty in generalising, and the lack of parameters to assess the quality of the results often make it difficult for non-experts to evaluate them. For several applications and scenarios, simple and more intuitive image-based approaches can be applied to support building inspections. This paper presents the StainView, which is a fast and reliable method. The method is based on the classification of the mosaic image, computed from a systematic acquisition, and allows one to (i) map stains in facades; (ii) locate critical areas; (iii) identify materials; (iv) characterise colours; and (v) produce detailed and comprehensive maps of results. The method was validated in three identical buildings in Bairro de Alvalade, in Lisbon, Portugal, that present different levels of degradation. The comparison with visual inspection demonstrates that StainView enables the automatic location and mapping of critical areas with high efficiency, proving to be a useful tool for building inspection: differences were of approximately 5% for the facade with the worst and average state of conservation, however, the values deteriorate for the facade under good conditions, reaching the double of percentage. In terms of processing speed, StainView allows a facade mapping that is 8–12 times faster, and this difference tends to grow with the number of evaluated façades.