Search Results (330)

Background/Objectives: Aortic-arch calcification (AAC) is a robust predictor of cardiovascular events often overlooked on routine chest radiographs (CXR). This systematic review aimed to evaluate the diagnostic accuracy of artificial intelligence (AI) models for detecting AAC on CXR and assess their potential for clinical implementation. Methods: The review followed PRISMA 2020 guidelines (PROSPERO: CRD420251208627). A search of Embase, PubMed, Scopus, and Web of Science was conducted (Jan 2020–Oct 2025) for studies evaluating AI models detecting AAC in adults. Bias was assessed using QUADAS-2. Due to methodological heterogeneity, a narrative synthesis was performed instead of a meta-analysis. Results: Out of 115 records, three retrospective studies (2022–2024) utilizing CNNs across ~2.7 million images were included. Models demonstrated high diagnostic discrimination (AUROC 0.81–0.99), though performance estimates were often attenuated in external cohorts. Pronounced sensitivity–specificity trade-offs occurred: one model achieved 95.9% recall, while another exhibited near-perfect specificity (0.99) despite markedly low sensitivity (0.22). Although the risk of bias was predominantly low, the overall GRADE certainty remained low due to methodological heterogeneity and the absence of cross-sectional imaging reference standards. Conclusions: Deep learning-based models reliably detect AAC on routine CXR, offering a scalable tool for opportunistic cardiovascular risk stratification. However, significant heterogeneity in model architectures and validation strategies currently limits broad comparability. Future research requires standardized annotation protocols and external validation to ensure clinical generalizability. Full article

Background/Objectives: Chest radiography is the primary first-line imaging tool for diagnosing pneumothorax in pediatric emergency settings. However, interpretation under clinical pressures such as high patient volume may lead to delayed or missed diagnosis, particularly for subtle cases. This study aimed to evaluate the diagnostic performance of ChatGPT-5, a multimodal large language model, in detecting and localizing pneumothorax on pediatric chest radiographs using multiple prompting strategies. Methods: In this retrospective study, 380 pediatric chest radiographs (190 pneumothorax cases and 190 matched controls) from a tertiary hospital were interpreted using ChatGPT-5 with three prompting strategies: instructional, role-based, and clinical-context. Performance metrics, including accuracy, sensitivity, specificity, and conditional side accuracy, were evaluated against an expert-adjudicated reference standard. Results: ChatGPT-5 achieved an overall accuracy of 0.77–0.79 and consistently high specificity (0.96–0.98) across all prompts, with stable reproducibility. However, sensitivity was limited (0.57–0.61) and substantially lower for small pneumothoraces (American College of Chest Physicians [ACCP]: 0.18–0.22; British Thoracic Society [BTS]: 0.41–0.46) than for large pneumothoraces (ACCP: 0.75–0.79; BTS: 0.85–0.88). The conditional side accuracy exceeded 0.96 when pneumothorax was correctly detected. No significant differences were observed among prompting strategies. Conclusions: ChatGPT-5 showed consistent but limited diagnostic performance for pediatric pneumothorax. Although the high specificity and reproducible detection of larger pneumothoraces reflect favorable performance characteristics, the unacceptably low sensitivity for subtle pneumothoraces precludes it from independent clinical interpretation and underscores the necessity of oversight by emergency clinicians. Full article

Background/Objective: Pulmonary impairments have been identified as some of the most complex and debilitating post-acute sequelae of SARS-CoV-2 infection (PASC) or long COVID. This study identified and characterised the specific forms of pulmonary impairments detected using pulmonary function tests (PFT), chest X-rays (CXR), and computed tomography (CT) scans in patients with long COVID symptoms. Methods: We conducted a single-centre retrospective study to evaluate 60 patients with long COVID who underwent PFT, CXR, and CT scans. Pulmonary function in long COVID patients was assessed using defined thresholds for key test parameters, enabling categorisation into normal, restrictive, obstructive, and mixed lung-function patterns. We applied exact binomial (Clopper–Pearson) 95% confidence intervals to calculate the proportions of patients falling below the defined thresholds. We also assessed the relationships among spirometric indices, lung volumes, and diffusion capacity (DLCO) using scatter plots and corresponding linear regressions. The findings from the CXRs and CT scans were categorised, and their prevalence was calculated. Results: A total of 60 patients with long COVID symptoms (mean age 60 ± 13 years; 57% female) were evaluated. The cohort was ethnically diverse and predominantly non-smokers, with a mean BMI of 32.4 ± 6.3 kg/m². PFT revealed that most patients had preserved spirometry, with mean Forced Expiratory Volume in 1 Second (FEV1) and Forced Vital Capacity (FVC) above 90% predicted. However, a significant proportion exhibited reductions in lung volumes, with total lung capacity (TLC) decreasing in 35%, and diffusion capacity (DLCO/TLCO) decreasing in 75%. Lung function pattern analysis showed 88% of patients had normal function, while 12% displayed a restrictive pattern; no obstructive or mixed patterns were observed. Radiographic assessment revealed that 58% of chest X-rays were normal, whereas CT scans showed ground-glass opacities (GGO) in 65% of patients and fibrotic changes in 55%, along with findings such as atelectasis, air trapping, and bronchial wall thickening. Conclusions: Spirometry alone is insufficient to detect impairment of gas exchange or underlying histopathological changes in patients with long COVID. Our findings show that, despite normal spirometry results, many patients exhibit significant diffusion impairment, fibrotic alterations, and ground-glass opacities, indicating persistent lung and microvascular damage. These results underscore the importance of comprehensive assessment using multiple diagnostic tools to identify and manage chronic pulmonary dysfunction in long COVID. Full article

Early and accurate diagnosis of lung diseases is essential for effective treatment and patient management. Conventional diagnostic models trained on a single data type often miss important clinical information. This study explored a multimodal deep learning framework that integrates cough sounds, chest radiograph (X-rays), and computed tomography (CT) scans to enhance disease classification performance. Two fusion strategies, early and intermediate fusion, were implemented and evaluated against three single-modality baselines. The dataset was collected from different sources. Each dataset underwent preprocessing steps, including noise removal, grayscale conversion, image cropping, and class balancing, to ensure data quality. Convolutional neural network (CNN) and Extreme Inception (Xception) architectures were used for feature extraction and classification. The results show that multimodal learning achieves superior performance compared with single models. The intermediate fusion model achieved 98% accuracy, while the early fusion model reached 97%. In contrast, single CXR and CT models achieved 94%, and the cough sound model achieved 79%. These results confirm that multimodal integration, particularly intermediate fusion, offers a more reliable framework for automated lung disease diagnosis. Full article

Background/Objectives: The Spinal Appearance Questionnaire (SAQ) assesses the self-perception of appearance of adolescents with idiopathic scoliosis (AIS). Due to originally scoring too many domains and producing prevalent ceiling effects when treated conservatively, the SAQv1.1, has been introduced. This study aimed to compare the test–retest reliability, convergent validity, and ceiling and floor effects of the two versions of the SAQ in patients with AIS. Methods: Conservatively treated females with AIS, aged 10–18 years old, were consecutively recruited from a scoliosis clinic. The Scoliosis Research Society-22 refined (SRS-22r), SAQ, and SAQv1.1 were collected, in English, with radiographic measurements (Cobb angle, Coronal Balance, and Vertebral Rotations). Nine domain scores were obtained from the original SAQ. Appearance, Expectations, and Total scores were calculated for SAQ v.1.1. Questionnaires were re-administered electronically after one week. Results: One hundred females, aged 13.9 ± 1.8 years with curve angles of 28.8° ± 13.9°, were included. The test–retest reliability for SAQ varied between domains (ICC_3,1 = 0.72 to 0.94). The Total, Appearance, and Expectation ICCs_3,1 for the SAQv1.1 were 0.92, 0.94, and 0.86, respectively. Convergent validity was demonstrated between seven SAQ domains and the SRS-22r Total and Cobb angle (|r| = 0.32 to 0.59). The SAQv1.1 Total correlated with the SRS-22r Total (r = −0.50) and with the Cobb angle (r = 0.56). All SAQ domains presented ceiling (Curve = 11% to Kyphosis = 68%) and floor effects (Chest = 8% and Waist = 4%). The SAQv1.1 Total and Appearance had low ceiling effects (≤5%), while Expectations presented both ceiling (14%) and floor effects (10%). Conclusions: The SAQv1.1 is recommended because of its stronger reliability, superior convergent validity, and fewer ceiling and floor effects in AIS. Full article

Background: Pneumothorax (PTX) develops in 1–2% of neonates, leading to significant morbidity and mortality and requiring providers to be comfortable with management. Our objective was to evaluate whether radiographic measurements of PTX size can be used to predict the need for procedural intervention in neonates in order to help guide the need for the availability of specific personnel. Methods: With the help of a data analyst, 62 patients diagnosed with neonatal PTX between March 2016 and October 2024 were identified. Most babies (46) were born in 2023–2024 when our new electronic health record could more easily identify these infants. PTX size was evaluated using radiographs by calculating the ratio of the widest transverse measurement of the PTX on both anteroposterior (AP) and, when available, lateral decubitus (DECUB) divided by the widest transverse measurement of the hemithorax above the diaphragm. Clinical data were collected, and statistical analysis was performed using need for intervention (thoracentesis (TC), chest tube (CT), or both). Results: We found that a larger PTX size ratio, measured in the AP (p < 0.0001) or DECUB view (p < 0.008), was highly associated with need for intervention in this cohort of infants with PTX. Only 33% of PTXs required intervention. Also, 13/14 (93%) cases who underwent TC ultimately required a CT. PTX was more prevalent in males in general, but sex was not associated with needing intervention. The average gestational age (GA) of the cohort was 36 5/7 weeks, with only 12% being < 34 weeks GA. Univariate analysis indicated that lower GA and birth weight were risk factors for intervention. There was a trend (p = 0.075, by Fisher’s exact test) suggesting that infants with both respiratory distress syndrome (RDS) and PTX may be more likely (60%) to require intervention (no RDS, 29% intervention). Finally, a receiver operator characteristic curve was derived from the AP ratio based on the yes/no intervention which resulted in an area under the curve statistic of 0.902 and the optimal AP ratio cutoff of 0.184. Conclusions: The ratio of the transverse measurement of the PTX/hemithorax size from radiographs was highly predictive for need for intervention in a cohort of primarily term infants with PTX. Smaller and lower GA infants were at a higher risk for requiring procedural intervention. Nearly all infants who had TC also needed a CT. These findings could inform clinical strategies for managing neonatal PTXs, especially in identifying appropriate needed personnel availability if a TC occurs. Full article

Background: Chest radiography is the most widely used diagnostic imaging modality globally, yet its interpretation is hindered by a critical shortage of radiologists, especially in low- and middle-income countries (LMICs). The interpretation is both time-consuming and error-prone in high-volume settings. Artificial Intelligence (AI) systems trained on public data may lack generalizability to multi-view, real-world, local images. Deep learning tools have the potential to augment radiologists by providing real-time decision support by overcoming these. Objective: We evaluated the diagnostic accuracy of a deep learning-based convolutional neural network (CNN) trained on multi-view, hybrid (public and local datasets) for detecting thoracic abnormalities in chest radiographs of adults presenting to a tertiary hospital, operating in offline mode. Methodology: A CNN was pretrained on public datasets (Vin Big, NIH) and fine-tuned on a local dataset from a Nepalese tertiary hospital, comprising frontal (PA/AP) and lateral views from emergency, ICU, and outpatient settings. The dataset was annotated by three radiologists for 14 pathologies. Data augmentation simulated poor-quality images and artifacts. Performance was evaluated on a held-out test set (N = 522) against radiologists’ consensus, measuring AUC, sensitivity, specificity, mean average precision (mAP), and reporting time. Deployment feasibility was tested via PACS integration and standalone offline mode. Results: The CNN achieved an overall AUC of 0.86 across 14 abnormalities, with 68% sensitivity, 99% specificity, and 0.93 mAP. Colored bounding boxes improved clarity when multiple pathologies co-occurred (e.g., cardiomegaly with effusion). The system performed effectively on PA, AP, and lateral views, including poor-quality ER/ICU images. Deployment testing confirmed seamless PACS integration and offline functionality. Conclusions: The CNN trained on adult CXRs performed reliably in detecting key thoracic findings across varied clinical settings. Its robustness to image quality, integration of multiple views and visualization capabilities suggest it could serve as a useful aid for triage and diagnosis. Full article

This study proposes a unified deep-learning framework for the concurrent classification of Chronic Obstructive Pulmonary Disease (COPD) severity and regression of the FEV1/FVC ratio from chest X-ray (CXR) images. We integrated a ConvNeXt-Large backbone with a Slot Attention mechanism to effectively disentangle and refine disease-relevant features for multi-task learning. Evaluation on a clinical dataset demonstrated that the proposed model with a 5-slot configuration achieved superior performance compared to standard CNN and Vision Transformer baselines. On the independent test set, the model attained an Accuracy of 0.9107, Sensitivity of 0.8603, and Specificity of 0.9324 for three-class severity stratification. Simultaneously, it achieved a Mean Absolute Error (MAE) of 8.2649 and a Mean Squared Error (MSE) of 151.4704, and an ${\mathrm{R}}^2$ of 0.7591 for FEV1/FVC ratio estimation. Qualitative analysis using saliency maps also suggested that the slot-based approach contributes to attention patterns that are more constrained to clinically relevant pulmonary structures. These results suggest that our slot-attention-based multi-task model offers a robust solution for automated COPD assessment from standard radiographs. Full article

Background: Accurate confirmation of nasogastric (NG) tubes is essential for patient safety, but delays and variability in interpretation remain common in clinical practice. Deep learning (DL) models have shown potential for assisting in this task, but real-world performance, particularly in detecting malpositioned tubes, remains insufficiently characterized. Methods: We conducted a pilot evaluation of a previously developed DL model using 135 chest radiographs from Kangwon National University Hospital. Expert physicians established the reference standard. Model performance was assessed and receiver operating characteristic (ROC) curve and precision recall curve (PRC) analyses were performed. Differences between correctly classified and misclassified cases were examined using Wilcoxon rank-sum and Fisher’s exact tests to explore potential clinical or radiographic contributors to model failure. Results: The model correctly classified 129 of 135 cases. The sensitivity was 96.1% (95% confidence interval (CI): 92.2–98.9%), specificity was 85.7% (95% CI: 42.2–97.7%), positive predictive value (PPV) was 99.2% (95% CI: 96.1–99.9%), negative predictive value (NPV) was 54.5% (95% CI: 25.4–80.8%), balanced accuracy was 90.8%, and F1-score was 0.976. The area under the ROC curve was 0.970 (95% CI: 0.929–1.000) and that under the PRC was 0.727 (95% CI: 0.289–1.000), reflecting substantial uncertainty related to the very small number of incomplete cases (n = 6). No statistically significant differences in clinical or radiographic characteristics were observed between correctly classified and misclassified cases. Conclusions: The DL model performed well in identifying correctly positioned NG tubes but demonstrated limited and unstable performance for detecting incomplete placements. Given the safety implications of misclassification, the model should be used only as an assistive tool with mandatory physician oversight. Larger, multi-center studies with greater representation of incomplete cases are required to obtain more reliable estimates and support safe clinical implementation. Full article

Background: Acute Respiratory Infections are a common reason for Pediatric Emergency Department (PED) visits. Differentiating bacterial and viral infections may be challenging and might result in incorrect antibiotic prescriptions and exacerbation of antimicrobial resistance. This study evaluated the impact of new diagnostic tests in PED. Methods: A retrospective cohort of 4882 acute febrile respiratory infection cases presenting to the PED was analyzed, comparing two periods: Period 1 (October 2016–March 2017, n = 2181) and Period 2 (October 2023–March 2024, n = 2701). During Period 1, Group A Streptococcus and Respiratory Syncytial Virus rapid antigen detection tests were available. During Period 2, new point-of-care tests (POCTs), including rapid C-reactive protein and rapid antigen detection for Influenza A, Influenza B, and SARS-CoV-2, and a multiplex PCR nasal swab, were introduced. Results: In Period 2, antibiotic prescriptions decreased by 28.4%, along with a reduction in broad-spectrum antibiotic use. A significant correlation was observed between reduced antibiotic prescription and the use of new POCTs and multiplex PCR tests. Performance of blood tests and chest radiographs also decreased. Conclusions: Implementing novel diagnostic tests in PED helps clinicians select more appropriate management options with an impact on reduced stress and radiation exposure and antibiotic prescription. Full article

Introduction: This study aims to predict mortality using chest radiographs obtained on the first day of intensive care admission, thereby contributing to better planning of doctors’ treatment strategies and more efficient use of limited resources through early and accurate predictions. Methods: We retrospectively analyzed 510 ICU patients. After data augmentation, a total of 3019 chest radiographs were used for model training and validation, while an independent, non-augmented test set of 100 patients (100 images) was reserved for final evaluation. Seventy-four (74) radiomic features were extracted from the images and analyzed using machine learning algorithms. Model performances were evaluated using the area under the ROC curve (AUC), sensitivity, and specificity metrics. Results: A total of 3019 data samples were included in the study. Through feature selection methods, the initial 74 features were gradually reduced to 10. The Subspace KNN algorithm demonstrated the highest prediction accuracy, achieving AUC 0.88, sensitivity 0.80, and specificity 0.87. Conclusions: Machine learning algorithms such as Subspace KNN and features obtained from PAAC radiographs, such as GLCM Contrast, Kurtosis, Cobb angle, Haralick, Bilateral Infiltrates, Cardiomegaly, Skewness, Unilateral Effusion, Median Intensity, and Intensity Range, are promising tools for mortality prediction in patients hospitalized in the internal medicine intensive care unit. These tools can be integrated into clinical decision support systems to provide benefits in patient management. Full article

Background/Objectives: This is the first prospective, same-day, multi-modality comparison of lung ultrasound (LUS) and chest radiography (CXR) for detecting acute heart failure (AHF) in non-critical patients with dyspnoea, examining the impact of chronic heart failure and pleural effusion, using low-dose chest CT (LDCT) as an objective comparator, and cardiologists-adjudicated AHF as reference standard. Methods: An observational study of 240 consecutive non-critical patients ≥50 years admitted with dyspnoea was conducted. Unstable AHF cases were deemed ineligible. Each modality was evaluated at the population level with area under the curve (AUC), sensitivity, and specificity, and compared at the patient level using conditional odds ratio for the association to AHF adjudicated by blinded cardiologists. Congestion was defined by LUS as (a) ≥3 B-lines bilaterally, or (b) B-lines combined with pleural effusion, and (c) CXR, interpreted by two thoracic radiologists, using (d) LDCT as an objective comparator. Results: Among 240 patients (66 with cardiologist-adjudicated AHF, 58 with chronic heart failure), LUS (b) demonstrated a diagnostic accuracy at population level of AUC = 0.82 (sensitivity = 80%, specificity = 84%), while CXR (c) achieved AUC = 0.80 (sensitivity = 68%, specificity = 91%), with CXR showing a modest but statistically significant difference over LUS at the patient level (OR = 1.51, p = 0.03). Incorporating pleural effusion into LUS increased its AUC from 0.67 to 0.82 (a vs. b, p < 0.001). The objective comparator, LDCT (d), achieved an AUC = 0.92 (sensitivity = 74%, specificity = 96%). In patients with chronic heart failure, LUS (b) and CXR (c) performed comparably (p = 0.87), whereas in those without chronic heart failure, CXR was superior (p = 0.04). Conclusions: In non-critical, diagnostically challenging patients with dyspnoea, in whom critical AHF cases were not eligible, including pleural effusion improved LUS accuracy for AHF. Diagnostic performance differed by heart failure history, with CXR superior in new-onset heart failure, while LUS and CXR performed comparably in chronic heart failure. Full article

Background: Discontinuing mechanical ventilation during cardiopulmonary bypass (CPB) is common but may adversely affect postoperative pulmonary function. This study aimed to evaluate the impact of stopping ventilation during CPB on postoperative gas exchange, radiographic findings, intensive care unit (ICU) length of stay (LOS), mortality, reintubation, re-exploration, and bleeding. Methods: A prospective observational study was performed involving adult patients scheduled for elective cardiac surgery requiring CPB. Participants were divided into ventilated and non-ventilated groups according to intraoperative strategy. Postoperative arterial carbon dioxide levels (PaCO₂), arterial partial pressure of oxygen (PaO₂), the PaO₂/FiO₂ ratio (P/F ratio), arterial oxygen saturation (SaO₂), and the ratio of PaCO₂ to minute ventilation (PaCO₂/MV) were measured before the induction of anesthesia (within 5 min after transportation into the operating room), postoperatively within 5–10 min after transportation to the ICU, and in a 24 h postoperative period. Chest X-ray data, mechanical ventilation time, LOS in ICU, re-exploration, reintubation, and bleeding parameters were documented. Analyses were also conducted with the estimation of the age effect and BMI. Results: Individuals in the non-ventilated group exhibited lower postoperative P/F ratios and elevated postoperative PaCO₂ and PaCO₂/MV ratios. The difference in gas exchange leveled off within 24 h. There was no difference in the incidence of atelectasis (postoperatively in a 24 h period), mechanical ventilation time, LOS in ICU, or mortality. However, the incidence of bleeding was higher in the non-ventilated group (χ² = 5.78, p = 0.016). Interestingly, postoperative PaCO₂ and PaCO₂/MV peaked in the 50-year age group. Conclusions: Continued mechanical ventilation during CPB correlates with better postoperative gas exchange, better CO₂ clearance, and fewer bleeding events. The results suggest that maintaining low tidal volume ventilation during CPB may provide benefits, especially for patients aged 50 years. Full article

Background/Objectives: In the elderly population, thoracic kyphosis often progresses with age, leading to secondary postural adaptations including scapular protraction, internal rotation, and anterior tilt. These alterations can potentially compromise shoulder biomechanics, particularly in patients undergoing reverse shoulder arthroplasty (RSA). The purpose of this study was to evaluate the relationship between thoracic sagittal alignment, quantified by the Cobb angle, and scapular internal rotation (SIR) assessed on CT scans in patients scheduled for RSA. Methods: A retrospective study was conducted on 164 patients who underwent RSA between 2016 and 2024 at a single tertiary referral center. Sagittal thoracic kyphosis was assessed using the Cobb angle measured on preoperative chest radiographs. SIR and anterior scapular tilt were evaluated using preoperative CT scans. Patients were divided into three groups according to the Cobb angle: Group A (≤36°), Group B (>36–46°), and Group C (≥47°). Statistical analysis was performed using the Spearman correlation coefficient and Kruskal–Wallis test, with a significance threshold set at p < 0.05. Results: Analysis demonstrated a weak but statistically significant positive correlation between age and SIR, as well as between thoracic kyphosis (Cobb angle) and SIR. Patients in Group C (Cobb angle ≥ 47°) showed higher mean SIR values (43.7°) compared to Group A (40.3°), with statistical significance achieved (p = 0.047). These findings suggest that greater thoracic kyphosis is associated with increased scapular internal rotation. No significant correlation was identified between anterior scapular tilt and thoracic kyphosis. Conclusions: This study reveals a correlation between increased thoracic kyphosis and greater scapular internal rotation in patients undergoing RSA. These postural and biomechanical alterations may have critical implications for surgical planning and postoperative outcomes. Preoperative assessment of sagittal spinal alignment, particularly thoracic kyphosis, should be integrated into the planning process for RSA to optimize implant positioning and improve functional results. Full article

Background: Silicosis is a lung disease caused by inhalation of crystalline silica dust, leading to lung fibrosis, respiratory symptoms, and impaired lung function. This study aimed to determine the prevalence of silicosis, asthma, and chronic obstructive pulmonary disease (COPD), and to identify factors associated with abnormal pulmonary function among residents living in dust-exposed areas in Thailand. Methods: A cross-sectional study was conducted from March 2024 to July 2024 among adults aged 18 years or older in Saraburi, Thailand. Data collected included demographics, comorbidities, respiratory symptoms, risk of silicosis, chest radiographs, and spirometry (forced vital capacity (FVC), forced expiratory volume in one second (FEV₁), and bronchodilator responsiveness (BDR)). Silicosis was confirmed based on a history of significant silica exposure and characteristic chest radiographic findings. Results: Among 290 participants (55.9% female, mean age 47.6 ± 16.4 years), the prevalence of silicosis, asthma, and COPD was 0.3%, 4.5%, and 10.3%, respectively. Abnormal chest radiographs were observed in 8.3%, and abnormal lung function in 34.1%, including restrictive lung patterns (16.6%), airway obstruction (9.0%), mixed defects (2.8%), and small-airway disease (5.9%). BDR was observed in 4.8%. Logistic regression identified increasing age as a significant predictor of abnormal lung function. Conclusions: Silicosis prevalence was lower than that of asthma and COPD, but abnormal pulmonary function—especially restrictive defects—was common. Notably, the prevalence of asthma and COPD was higher than previously reported community-based diagnosis rates, suggesting potential underdiagnosis. Older age was associated with a higher likelihood abnormal lung function. These findings highlight the need for targeted surveillance, preventive measures, and public health interventions to mitigate the respiratory impacts of dust exposure in community settings Full article