Background: Our study aimed to explore the way artificial intelligence (AI) utilization is perceived in pediatric medicine, examining its acceptance among patients (in this case represented by their adult parents), and identify the challenges it presents in order to understand the factors influencing its adoption in clinical settings. Methods: A structured questionnaire was applied to caregivers (parents or grandparents) of children who presented in tertiary pediatric clinics. Results: The most significant differentiations were identified in relation to the level of education (e.g., aversion to AI involvement was 22.2% among those with postgraduate degrees, 43.9% among those with university degrees, and 54.5% among those who only completed high school). The greatest fear among respondents regarding the medical use of AI was related to the possibility of errors occurring (70.1%). Conclusions: The general attitude toward the use of AI can be considered positive, provided that it remains human-supervised, and that the technology used is explained in detail by the physician. However, there were large differences among groups (mainly defined by education level) in the way AI is perceived and accepted.

In recent years, our clinical practice as paediatric pulmonologists has focused on refining strategies to optimise pulmonary function tests (PFTs) for preschool children, particularly those aged as young as 2 years and 5 months. This viewpoint reflects on our experience conducting over 7000 spirometry and impulse oscillometry (IOS) tests, sharing practical insights into achieving high success rates with young children. We emphasise the importance of creating a child-friendly, minimally stimulating environment, tailored communication using familiar and engaging language, and leveraging gamification and positive reinforcement. In particular, we highlight the use of role-playing as an effective engagement strategy, allowing children to teach a stuffed toy how to perform the test. Furthermore, we differentiate the approaches required for spirometry, which demands active cooperation, and IOS, which allows for tidal breathing and is more suitable for younger or less cooperative children. Through these strategies, we have achieved consistent, technically acceptable results in preschool-aged children, aligning with the latest standardisation guidelines. Our findings demonstrate the value of adaptability, patience and creativity in paediatric PFTs, suggesting that these approaches improve not only test accuracy but also the overall experience for both children and caregivers. Future research should further investigate these techniques to enhance paediatric pulmonary testing protocols.

Patients with chronic lung conditions, including cystic fibrosis, may be prone to severe COVID-19. Therefore, therapeutic intervention should be prompt and tailored to all associated comorbidities. We report the case of a 17-year-old male adolescent with cystic fibrosis and multiple chronic conditions (bronchiectasis, exocrine pancreatic insufficiency, chronic multidrug resistant Pseudomonas aeruginosa colonization, nasal polyposis, chronic sinusitis, ventricular extrasystoles and multiple drug allergies), who presented with an acute episode of productive cough, and was confirmed with moderate COVID-19 based on positive RT-PCR for SARS-CoV-2 and lung imaging showing isolated foci of interstitial pneumonia. Intravenous treatment with the monoclonal antibody cocktail casirivimab and imdevimab was administered. The evolution was favorable, with rapid remission of the inflammatory syndrome and gradual decrease of cough, without progression to severe or critical COVID-19, but with complications such as repeated hemoptysis, which was due to the patient’s underlying conditions, and which required close monitoring for timely adjustment of the patient’s chronic treatment.

Background: Acute bronchiolitis is among the most common causes of lower respiratory tract infection in infancy and remains a clinically heterogeneous syndrome. Current international guidelines recommend supportive management and generally discourage routine bronchodilator administration; however, physiological responsiveness may exist in a subset of patients. Tidal breathing analysis (TBA) provides a non-invasive, bedside method for objectively assessing respiratory mechanics and may objectively quantify physiological changes following bronchodilator administration. Case Presentation: We report a 23-month-old boy presenting with severe RSV bronchiolitis-like wheezing illness complicated by acute respiratory failure. Bronchodilator responsiveness was evaluated using predefined TBA parameters before and after inhaled bronchodilator administration. Following treatment, time to peak tidal expiratory flow (tPTEF) increased from 0.17 to 0.33 s (+94%), while the tPTEF/TE ratio increased from 0.12 to 0.27 (+125%). Expiratory time (TE) and total respiratory cycle duration (Ttot) decreased by 12.6% and 5.1%, respectively, whereas tidal volume (VT) and minute ventilation (MV) increased by 12.1% and 18.3%. Peak expiratory flow remained unchanged (0.14 L/s before and after bronchodilator administration). Discussion: To our knowledge, this is among the first reports using bedside TBA to characterize acute bronchodilator responsiveness in severe bronchiolitis-like illness. The improvement in expiratory timing indices, accompanied by shorter expiratory time without changes in peak expiratory flow, is consistent with improved expiratory flow dynamics following bronchodilator administration. These findings support the hypothesis that a bronchodilator-responsive physiological phenotype may exist. Previous studies have demonstrated an association between severe bronchiolitis during infancy and subsequent recurrent wheezing or asthma; however, whether acute bronchodilator responsiveness, measured objectively by TBA, represents a testable hypothesis for future longitudinal studies. Conclusions: This case illustrates the feasibility of bedside TBA for identifying physiological bronchodilator responsiveness in severe bronchiolitis-like illness. Prospective longitudinal studies are warranted to determine whether TBA-defined responsiveness represents an early physiological marker requiring prospective validation and whether such physiological stratification may support individualized follow-up strategies.

In the field of medical imaging, techniques like computed tomography (CT), magnetic resonance imaging (MRI), and X-rays are essential for diagnosing and monitoring diseases, particularly in pulmonary pathology. A significant development in this area is the application of automated segmentation and machine learning, enhancing the accuracy in diagnosing lung diseases, such as lung cancer. However, there remains a knowledge gap in fully understanding the potential and limitations of these technologies, especially across diverse clinical conditions and datasets. To address this gap, the paper delves into the integration of artificial intelligence with conventional imaging techniques, focusing primarily on the use of convolutional neural networks (CNNs) and transformer-based models in automated segmentation. This approach is pivotal in improving the detection rates and accuracy of diagnoses in complex pulmonary diseases. Findings indicate that AI-enhanced imaging significantly advances the early detection of pulmonary diseases, notably lung cancer, and reduces the time until diagnosis. Yet, challenges such as the necessity for diverse and comprehensive training data and the generalizability of algorithms, persist. Moreover, ethical considerations in the deployment of AI technologies in healthcare are crucial. In conclusion, while these technologies mark substantial progress in pulmonary imaging, it is essential to find the balance between technological advancements and ethical considerations. This balance is key to ensuring effective and equitable healthcare, maximizing the benefits of AI in medical imaging while maintaining patient trust and privacy.

Preschool spirometry needs age-tailored FEVt indices/limits, low-dead-space hardware, AI-assisted QC, physician-led coaching and caregiver prep. Other tests may complement selectively. Expand GLI-Junior reference norms to strengthen interpretation. https://bit.ly/47ajdbH