Background: Development of new tools in artificial intelligence has an outstanding performance in the recognition of multidimensional patterns, which is why they have proven to be useful in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD). Methods: This was an observational analytical single-centre study in patients with spirometry performed in outpatient medical care. The segment that goes from the peak expiratory flow to the forced vital capacity was modelled with quadratic polynomials, the coefficients obtained were used to train and test neural networks in the task of classifying patients with COPD. Results: A total of 695 patient records were included in the analysis. The COPD group was significantly older than the No COPD group. The pre-bronchodilator (Pre BD) and post-bronchodilator (Post BD) spirometric curves were modelled with a quadratic polynomial, and the coefficients obtained were used to feed three neural networks (Pre BD, Post BD and all coefficients). The best neural network was the one that used the post-bronchodilator coefficients, which has an input layer of 3 neurons and three hidden layers with sigmoid activation function and two neurons in the output layer with softmax activation function. This system had an accuracy of 92.9% accuracy, a sensitivity of 88.2% and a specificity of 94.3% when assessed using expert judgment as the reference test. It also showed better performance than the current gold standard, especially in specificity and negative predictive value. Conclusion: Artificial Neural Networks fed with coefficients obtained from quadratic and cubic polynomials have interesting potential of emulating the clinical diagnostic process and can become an important aid in primary care to help diagnose COPD in an early stage.

Chronic obstructive pulmonary disease (COPD) is one of the top causes of morbidity and mortality worldwide. Although for many years its accurate diagnosis has been a focus of intense research, it is still challenging. Due to its simplicity, portability, and low cost, spirometry has been established as the main tool to detect this condition, but its flawed performance makes it an imperfect COPD diagnosis gold standard. This review aims to provide an up-to-date literature overview of recent studies regarding COPD diagnosis; we seek to identify their limitations and establish perspectives for spirometric diagnosis of COPD in the XXI century by combining deep clinical knowledge of the disease with advanced computer analysis techniques.

Introduction: The maximum oxygen consumption (VO2max) is the gold standard in the cardiorespiratory endurance assessment. Objective: This study aimed to develop a mathematical model that contains variables to determine the VO2max of sedentary people. Methods: Twenty participants (10 men and 10 women) with a mean age of 19.8±1.77 years were included. For each participant, body composition (percentage of fat and muscle), heart rate variability (HRV) at rest (supine and standing), and VO2max were evaluated through an indirect test on a cycloergometer. A multivariate linear regression model was developed from the data obtained, and the model assumptions were verified. Results: Using the data obtained, including percentage of fat (F), percentage of muscle (M), percentage of power at very low frequency (VLF), α-value of the detrended fluctuation analysis (DFAα1), heart rate (HR) in the resting standing position, and age of the participants, a model was established for men, which was expressed as VO2max = 4.216 + (Age*0.153) + (F*0.110) - (M*0.053) - (VLF*0.649) - (DFAα1*2.441) - (HR*0.014), with R2 = 0.965 and standard error = 0.146 L/min. For women, the model was expressed as VO2max = 1.947 - (Age*0.047) + (F*0.024) + (M*0.054) + (VLF*1.949) - (DFAα1*0.424) - (HR*0.019), with R2 = 0.987 and standard error = 0.077 L/min. Conclusion: The obtained model demonstrated the influence exerted by body composition, the autonomic nervous system, and age in the prediction of VO2max.

Many decisions must be made under stress; therefore, stress and decision-making are intrinsically related not only at the behavioral level but also at the neural level. Additionally, virtual reality tools have been proposed as a method to induce stress in the laboratory. This review focuses on answering the following research question: Does stress assessed by physiological variables of a subject under virtual reality stimuli increase the chances of error in decision-making? The reviewed studies were consulted in the following databases: PubMed, IEEE Xplore, and Science Direct. The analysis of the consulted literature indicates that the stress induced in the laboratory using virtual reality tools and the physiological response of the central and autonomous nervous system are complementary subjects and allow the design of training and support systems for the decision-making process

Background Poor assessment of anaesthetic depth (AD) has led to overdosing or underdosing of the anaesthetic agent, which requires continuous monitoring to avoid complications. The evaluation of the central nervous system activity and autonomic nervous system could provide additional information on the monitoring of AD during surgical procedures. Methods Observational analytical single-centre study, information on biological signals was collected during a surgical procedure under general anaesthesia for signal preprocessing, processing and postprocessing to feed a pattern classifier and determine AD status of patients. The development of the electroencephalography index was carried out through data processing and algorithm development using MATLAB V.8.1. Results A total of 25 men and 35 women were included, with a total time of procedure average of 109.62 min. The results show a high Pearson correlation between the Complexity Brainwave Index and the indices of the entropy module. A greater dispersion is observed in the state entropy and response entropy indices, a partial overlap can also be seen in the boxes associated with deep anaesthesia and general anaesthesia in these indices. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. Conclusion Biological signal filtering and a machine learning algorithm may be used to classify AD during a surgical procedure. Further studies will be needed to confirm these results and improve the decision-making of anaesthesiologists in general anaesthesia.