Mining slopes, electrical power generation dams and several big construction enterprises demands continuous inspections. The size and diversity of these structures demands high precision and portable approach. In such environments, 3D reconstruction methodologies are able to capture and analyze the real world in detail. However, the accuracy and precision can affect the ability to process and interpret the acquired data. For instance, laser scanning is a very accurate method and can deliver a higher quality result. Meanwhile, 3D photogrammetry using a single camera and Structure From Motion (SFM) have their performance correlated with the image quality. In a typical application, 3D data from reconstruction is pre-processed by a specialist. Then, it is stored for comparison and analyzed over time. The posterior analysis has several challenges associated with the reconstruction process characteristics. Several techniques have been developed to allow the comparison of point cloud captured at different epochs. Therefore, this research work presents a new methodology to perform alignment and comparison of point clouds, namely 3D-CP2, an acronym for 3D Correspondence and Point Projection. This method intends to analyze the point cloud motion to be applied in terrestrial 3D SFM reconstructions. Besides, the technique can also be used in many other related applications. The methodology developed in this work is applied in controlled experiments and real use cases to show its potential for point cloud displacements analysis. The results showed that the proposed method is efficient and can produce results more accurately than the referenced literature.

Objetive. Hypertension, a leading contributor to cardiovascular morbidity, underscores the need for accurate and continuous blood pressure (BP) monitoring. Photoplethysmography (PPG) emerges as a promising approach for continuous BP monitoring. However, the precision of BP estimates derived from PPG signals has been the subject of ongoing debate, requiring a comprehensive evaluation of their efficacy. This paper aims to provide the potentials and limitations regarding BP estimation from single-site PPG signals. Approach. We developed a calibration-based Siamese ResNet model for BP estimation. We compared the use of normalized PPG (N-PPG) against the normalized invasive arterial BP (N-IABP) signals as input. N-IABP signals, while not directly presenting systolic (SBP) and diastolic (DBP) BP values, are expected to offer more precise estimations than PPG since it is a direct pressure sensor inside the body. Thus, if N-IABP poses challenges in BP estimation, predicting BP from PPG signals might be even more challenging. Main results. Our evaluation, conducted using the AAMI and BHS standards on the VitalDB dataset, revealed that inference using N-IABP signals meet with AAMI standards for both SBP and DBP, with errors of 1.29 ± 6.33 mmHg for systolic pressure and 1.17 ± 5.78 for diastolic pressure. In contrast, N-PPG based inference exhibited inferior performance than N-IABP, presenting 1.49 ± 11.82 mmHg and 0.89 ± 7.27 mmHg for systolic and diastolic pressure respectively in their best setup. Significance. Our findings establish a critical benchmark for PPG performance, providing realistic expectations for its BP estimation capabilities. We concluded that while PPG signals contain BP-correlated information, they may not suffice for accurate prediction.

Cardiovascular diseases are the leading causes of death, and blood pressure (BP) monitoring is essential for prevention, diagnosis, assessment, and treatment. Photoplethysmography (PPG) is a low-cost opto-electronic technique for BP measurement that allows the acquisition of a modulated light signal highly correlated with BP. There are several reports of methods to estimate BP from PPG with impressive results; in this study, we demonstrate that the previous results are excessively optimistic because of their train/test split configuration. To manage this limitation, we considered intra- and inter-subject data arrangements and demonstrated how they affect the results of feature-based BP estimation algorithms (i.e., XGBoost, LightGBM, and CatBoost) and signal-based algorithms (i.e., Residual U-Net, ResNet-18, and ResNet-LSTM). Inter-subject configuration performance is inferior to intra-subject configuration performance, regardless of the model. We also showed that, using only demographic attributes (i.e., age, sex, weight, and subject index number), a regression model achieved results comparable to those obtained in an intra-subject scenario.Although limited to a public clinical database, our findings suggest that algorithms that use an intra-subject setting without a calibration strategy may be learning to identify patients and not predict BP.

The electrocardiogram (ECG) serves as a valuable diagnostic tool, providing crucial information about life-threatening cardiac conditions such as atrial fibrillation and myocardial infarction. A prompt and efficient assessment of ECG exams in environments such as Emergency Rooms (ERs) can significantly enhance the chances of survival for high-risk patients. Despite the presence of numerous works on ECG classification, most of these studies have concentrated on one-dimensional ECG signals, which are commonly found in publicly available ECG datasets. Nevertheless, the practical relevance of such methods is limited in hospital settings, where ECG exams are usually stored as images. In this study, we have developed an artificial intelligence-driven screening system specifically designed to analyze 12-lead ECG images. Our proposed method has been trained on an extensive dataset comprising 99,746 12-lead ECG exams collected from the ambulatory section of a tertiary hospital. The primary goal was to precisely classify the exams into three classes: Normal (N), Atrial Fibrillation (AFib), and Other (O). The evaluation of our approach yielded AUROC scores of 93.2%, 99.2%, and 93.1% for N, AFib, and O, respectively. To further validate our approach, we conducted evaluations using the 2018 China Physiological Signal Challenge (CPSC) database. In this evaluation, we achieved AUROC scores of 91.8%, 97.5%, and 70.4% for the classes N, AFib, and O, respectively. Additionally, we assessed our method using 1,074 exams acquired in the ER and obtained AUROC values of 98.3%, 98.0%, and 97.7% for the classes N, AFib, and O, respectively. Furthermore, we developed and deployed a system with a trained model within the ER of a tertiary hospital for research purposes. This system automatically retrieves newly captured ECG chart images from the Picture Archiving and Communication System (PACS) within the ER. These images undergo necessary preprocessing steps and serve as input for our proposed classification method. This comprehensive approach established an efficient and versatile end-to-end framework for ECG classification. The results of our study highlight the potential of leveraging artificial intelligence in the screening of ECG exams, offering a promising solution for the rapid assessment and prioritization of patients in the ER.