Background: COPD is a chronic disease characterized by frequent exacerbations that require hospitalization, significantly increasing the care burden. In recent years, the use of artificial intelligence-based tools to improve the management of patients with COPD has progressed, but the prediction of readmission has been less explored. In fact, in the state of the art, no models specifically designed to make medium-term readmission predictions (2–3 months after admission) have been found. This work presents a new intelligent clinical decision support system to predict the risk of hospital readmission in 90 days in patients with COPD after an episode of acute exacerbation. Methods: The system is structured in two levels: the first one consists of three machine learning algorithms —Random Forest, Naïve Bayes, and Multilayer Perceptron—that operate concurrently to predict the risk of readmission; the second level, an expert system based on a fuzzy inference engine that combines the generated risks, determining the final prediction. The employed database includes more than five hundred patients with demographic, clinical, and social variables. Prior to building the model, the initial dataset was divided into training and test subsets. In order to reduce the high dimensionality of the problem, filter-based feature selection techniques were employed, followed by recursive feature selection supported by the use of the Random Forest algorithm, guaranteeing the usability of the system and its potential integration into the clinical environment. After training the models in the first level, the knowledge base of the expert system was determined on the training data subset using the Wang–Mendel automatic rule generation algorithm. Results: Preliminary results obtained on the test set are promising, with an AUC of approximately 0.8. At the selected cutoff point, a sensitivity of 0.67 and a specificity of 0.75 were achieved. Conclusions: This highlights the system’s future potential for the early identification of patients at risk of readmission. For future implementation in clinical practice, an extensive clinical validation process will be required, along with the expansion of the database, which will likely contribute to improving the system’s robustness and generalization capacity.

Background and Objectives: Spirometry is the most widely used pulmonary function test for diagnosing respiratory diseases. Its progressive incorporation into non-specialized settings, such as primary care, raises challenges for ensuring the reliability of results. In this context, tools based on artificial intelligence (AI) techniques have emerged as promising solutions to support quality control in spirometry. This systematic review aims to synthesize the available evidence on their application in this field. Methods: A systematic search was conducted in PubMed and IEEE Xplore to identify peer-reviewed original studies, published between 2014 and June 2025, that applied AI to spirometry quality control. The search and data extraction followed the PRISMA guidelines. Results: Six studies met the inclusion criteria. Four analyzed the acceptability and usability of the maneuver, and two focused on detecting errors committed during test performance. The most widely used models were convolutional neural networks, used in four studies, whereas two studies employed other conventional machine learning models. Three models reported area under the ROC curve values higher than 0.88. Conclusions: AI-based tools show great potential to assist in spirometry quality control, both in determining acceptability and in detecting errors. However, current studies remain scarce and highly heterogeneous in both objectives and methods. Broader, multicenter research, including validation in non-specialized settings, is required to confirm their clinical utility and facilitate their implementation in clinical practice.

Background: Chronic Obstructive Pulmonary Disease (COPD) often leads to acute exacerbations requiring hospitalization. While artificial intelligence (AI) has been increasingly used to improve COPD management, predicting whether the length of hospital stay (LOHS) will exceed clinically relevant thresholds remains insufficiently explored. Methods: This study presents a novel clinical decision support system to predict whether LOHS following an acute exacerbation will surpass specific cutoffs (6 or 10 days). The approach involves two stages: (1) clinical, demographic, and social variables are encoded into structured signals and transformed into spectrogram-like images via a pipeline based on sinusoidal encoding and Mel-frequency cepstral coefficients (MFCCs); and (2) these images are fed into a Convolutional Neural Network (CNN) to estimate the probability of extended hospitalization. Feature selection with XGBoost reduced dimensionality to 16 variables. The model was trained and tested on a dataset of over 500 patients. Results: On the test set, the model achieved an AUC of 0.77 for predicting stays longer than 6 days and 0.75 for stays over 10 days. Sensitivity and specificity were 0.79/0.72 and 0.74/0.80, respectively. Conclusions: This system leverages image-based data formalization to predict LOHS in COPD patients, facilitating early risk stratification and more informed clinical planning. Results are promising, but external validation with larger and more diverse datasets remains essential.

Background: Spirometry is the most widely used pulmonary function test for diagnosing respiratory diseases. However, the quality of the results mainly depends on the correct execution of the maneuver, making quality control essential. Traditionally, this process relies on subjective and laborious visual inspection. Methods: To overcome these limitations, this work proposes an intelligent clinical decision support system to assist in spirometry quality control. The proposed system generates a graphical construct that integrates the spirometry curves (flow-volume and volume-time curves) along with patient demographic information (sex, age, and BMI) extracted from the spirometry report. The resulting image is processed by a convolutional neural network based on the ResNet-18 architecture, whose output quantifies the risk of the performed test being unacceptable. This approach allows for simple integration of the system into clinical practice while accounting for individual patient characteristics during classification. Results: The results obtained in the test set are promising, with an AUC of 0.94 (95% CI: 0.80–1.00) and a sensitivity and specificity at the selected cut-off point of 75.00% (95% CI: 40–100%) and 100.00% (95% CI: 100–100%), respectively. Conclusions: Despite this, it should be noted that the system is still in a conceptual phase of development and therefore requires broader validation in real clinical environments as well as the incorporation of more diverse datasets to evaluate its robustness and generalization before its final implementation.

Context and objectives: Lung ultrasound (LUS) is a safe low-cost tool that enables diagnosis, monitoring and guidance for interventional procedures at the patient's bedside. However, its expansion is hindered by a lack of training programs and the inherent difficulty of interpreting ultrasound images. In this context, Artificial Intelligence (AI) is emerging as a supportive tool for LUS interpretation, ensuring diagnostic efficacy and mitigating the shortage of experts. This systematic review aims to summarize and analyse recent advances in AI-based tools to support LUS interpretation. Methods: A systematic literature search was conducted across Web of Science, IEEE Xplore, and PubMed databases to identify peer-reviewed original journal articles published between 2015 and November 2025 that employed AI for the identification and localization of lung artifacts, anatomical structures, and pathological findings. Results: Twenty-four studies were included, identifying three main strategies: segmentation (10 studies), object detection (4 studies), and the generation of visual explanations through saliency maps (10 studies). All employed CNN-based architectures. The evaluation metrics used were heterogeneous. Conclusions: The development of AI systems to support LUS interpretation shows high potential; however, current studies exhibit significant heterogeneity in their objectives, methodologies, and evaluation metrics. It is necessary to move towards solutions designed for specific clinical environments and to adopt standardized protocols and evaluations that facilitate their implementation in clinical practice.

Background and Objectives: Non-invasive mechanical ventilation is the first-line treatment for managing acute respiratory failure. However, patient variability and complex pulmonary mechanics complicate therapy adjustments, frequently leading to ventilator-induced lung injuries. This study aims to propose and define a simulation platform and decision support prototype, named VENTILA2, to optimize pressure-controlled ventilation strategies. Methods: The system integrates a bicompartmental series model of the respiratory system incorporating severity-stratified physiological profiles of chronic obstructive pulmonary disease and acute respiratory distress syndrome, and it is coupled with a Mamdani fuzzy inference system. This architecture maps inspiratory time adjustments based on pressure errors and their derivatives across predefined clinical profiles within a scenario-based feedforward parameter-mapping framework. Results: Evaluated through quantitative operational verification across all profiles and proof-of-concept case studies, the platform successfully recreates complex clinical scenarios, accurately simulating phenomena such as accelerated lung emptying in severe acute respiratory distress syndrome and air trapping in moderate chronic obstructive pulmonary disease. Conclusions: VENTILA2 provides a controlled simulation environment for evaluating pathology-specific ventilatory configurations across simulated profiles prior to clinical implementation, though it remains an early-stage prototype whose clinical effectiveness, safety, and robustness remain to be rigorously evaluated.