Regarding data collection for Life Cycle Assessment (LCA), the traditional method remains manual, which can be complemented by exploring data in existing databases; however, this approach compromises its reliability and accuracy. It is argued that the use of new technologies, such as Building Information Modeling (BIM), the Internet of Things (IoT), and Digital Twins (DT), provides greater accuracy in LCA. This article aims to quantify the impact on the LCA primary energy indicator, distinguishing between non-renewable and renewable sources, with the support of integrating IoT into BIM and DT. Comparative analyses of energy consumption were carried out in a Case Study. The case unit was a research and teaching laboratory specializing in construction materials. IoT monitoring was done by instantiating the data in a corresponding Digital Twin, aiming for real-time visualization in a BIM model. We observed that the LCA Indicators of primary energy consumption, based on estimated data, are extremely high, approximately 528% higher in the material characterization and concrete and mortar testing environment. We highlight a potential scale of accuracy that IoT-mediated LCA data collection can achieve. We demonstrate the application of three IoT and DT theoretical frameworks in an integrated way for architecture, engineering, and construction. We exemplify how IoT will promote Construction 4.0, generating an intelligent and invisible global platform that links physical and digital entities to enable a new class of applications and services. This investigation is aligned with two critical national strategies: BIM BR and IoT.

Flow-Mediated Dilation (FMD) is a non-invasive technique used to assess endothelial function and, thereby, estimate atherosclerosis risk before the onset of its clinical manifestations. Cardiovascular disease remains the leading cause of death globally, responsible for approximately 17.9 million deaths annually, making early and accurate detection of cardiovascular risk critical. However, conventional FMD analysis relies on semi-automatic or manual measurement of arterial lumen diameter, which introduces observer variability and limits reproducibility. In this study, we propose an automated framework for brachial artery segmentation in FMD ultrasound videos based on deep learning. A dataset of 186 FMD examinations (920 manually annotated frames) was collected at the Clinical Research Center of the University of Campinas (UNICAMP) and split into 80% training and 20% validation sets. Three segmentation architectures were evaluated and compared: CMUNeXt, CMUNeXt-S, and CMUNeXt-L. The Deep Learning architecture CMUNeXt-L achieved the best performance among the models evaluated in this study, reaching an IoU of 0.9515. Furthermore, its results were superior to most traditional segmentation methods reported in the literature, including Rectangular Active Contour (Dice = 0.9335), Modified Affinity Propagation (Dice = 0.9213), and Cubic Splines Active Contour (Dice = 0.9170), while showing performance comparable to the Hough Transform combined with Canny edge detection approach (Dice = 0.9523). These results demonstrate that modern convolutional architectures with large-kernel inverted bottlenecks can substantially reduce inter-observer variability and accelerate the analysis of FMD examinations, supporting the use of FMD as a surrogate endpoint for clinical trials and its broader adoption in clinical practice.