Publicaciones en Revistas
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[1] Khalili, E., Priego-Torres, B. , León-Jiménez, A. and Sanchez-Morillo, D. (2024). Automatic Lung Segmentation in Chest X-Ray Images Using SAM With Prompts From YOLO. IEEE Access, vol. 12, pp. 122805-122819. https://doi.org/10.1109/ACCESS.2024.3454188.
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IEEE
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Despite the impressive performance of current deep learning models in the field of medical imaging, transferring the lung segmentation task in X-ray images to clinical practice is still a pending task. In this study, the performance of a fully automatic framework for lung field segmentation in chest X-ray images was evaluated. The framework is rooted in the combination of the Segment Anything Model (SAM) with prompt capabilities, and the You Only Look Once (YOLO) model to provide effective prompts. Transfer learning, loss functions, and several validation strategies were thoroughly assessed. This provided a complete benchmark that enabled future research studies to fairly compare new segmentation strategies. The results achieved demonstrated significant robustness and generalization capability against the variability in sensors, populations, disease manifestations, device processing, and imaging conditions. The proposed framework was computationally efficient, could address bias in training over multiple datasets, and had the potential to be applied across other domains and modalities. |
[2] Sanchez‐Morillo, D., León‐Jiménez, A., Guerrero‐Chanivet, M., Jiménez‐Gómez, G., Hidalgo‐Molina, A., & Campos‐Caro, A. (2024). Integrating routine blood biomarkers and artificial intelligence for supporting diagnosis of silicosis in engineered stone workers. Bioengineering & Translational Medicine. https://doi.org/10.1002/btm2.10694
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WILEY
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Engineered stone silicosis (ESS), primarily caused by inhaling respirable crystalline silica, poses a significant occupational health risk globally. ESS has no effective treatment and presents a rapid progression from simple silicosis (SS) to progressive massive fibrosis (PMF), with respiratory failure and death. Despite the use of diagnostic methods like chest x-rays and high-resolution computed tomography, early detection of silicosis remains challenging. Since routine blood tests have shown promise in detecting inflammatory markers associated with the disease, this study aims to assess whether routine blood biomarkers, coupled with machine learning techniques, can effectively differentiate between healthy individuals, subjects with SS, and PMF. To this end, 107 men diagnosed with silicosis, ex-workers in the engineered stone (ES) sector, and 22 healthy male volunteers as controls not exposed to ES dust were recruited. Twenty-one primary biochemical markers derived from peripheral blood extraction were obtained retrospectively from clinical hospital records. Relief-F features selection technique was applied, and the resulting subset of 11 biomarkers was used to build five machine learning models, demonstrating high performance with sensitivities and specificities in the best case greater than 82% and 89%, respectively. The percentage of lymphocytes, the angiotensin-converting enzyme, and lactate dehydrogenase indexes were revealed, among others, as blood biomarkers with significant cumulative importance for the machine learning models. Our study reveals that these biomarkers could detect a chronic inflammatory status and potentially serve as a supportive tool for the diagnosis, monitoring, and early detection of the progression of silicosis. |
Khalili, E., Sanchez-Morillo, D., Priego-Torres, B., & León-Jiménez, A. (2025). Localization and classification of abnormalities on chest X-ray images using a Mamba-YOLOvX model. Expert Systems with Applications, 284. https://doi.org/10.1016/J.ESWA.2025.127929
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Expert Systems with Applications
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Chest X-rays (CXR) are critical diagnostic tools for detecting thoracic abnormalities. However, challenges such as overlapping anatomical structures, class imbalance, and dataset heterogeneity hinder accurate interpretation and limit model generalizability. To address these issues, a Mamba-YOLOvX model is presented in this study. It was aimed to integrate global and local lesion information to improve the detection and localization of thoracic abnormalities. The model incorporates novel architectural improvements, including combined spatial and channel attention mechanisms and selective scanning blocks, to capture fine-grained features and enhance multi-scale detection. In addition, a projection-based data augmentation strategy, leveraging rib segmentation and keypoint alignment was developed to improve the anatomical consistency and the intensity normalization across datasets. Extensive experiments were conducted on three large-scale datasets (VinDr-CXR, ChestX-ray8, and CXR-AL14), achieving state-of-the-art performance in detecting abnormalities of varying sizes. The proposed method reached an average precision at 50 % intersection over union of 0.366, 0.153, and 0.615 on the VinDr-CXR, ChestX-ray8, and CXR-AL14 datasets, respectively. Results demonstrated significant improvements in precision, recall, and mean average precision, particularly for small lesions. Cross-dataset validation confirmed the model’s robustness and generalizability. This study highlights the potential of integrating advanced deep learning techniques with domain-specific augmentations to enhance clinical decision support systems for thoracic disease detection. By addressing critical challenges such as class imbalance, annotation inconsistencies, and scale variations, the enhanced Mamba-YOLOvX model is shown as a scalable, accurate, and generalizable solution for automated CXR analysis. |
Priego-Torres, B., Sanchez-Morillo, D., Khalili, E., Conde-Sánchez, M. Á., García-Gámez, A., & León-Jiménez, A. (2025). Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays. Computers in Biology and Medicine, 191. https://doi.org/10.1016/J.COMPBIOMED.2025.110153. https://hdl.handle.net/10498/36175
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Computers in Biology and Medicine
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Silicosis, a debilitating occupational lung disease caused by inhaling crystalline silica, continues to be a significant global health issue, especially with the increasing use of engineered stone (ES) surfaces containing high silica content. Traditional diagnostic methods, dependent on radiological interpretation, have low sensitivity, especially, in the early stages of the disease, and present variability between evaluators. This study explores the efficacy of deep learning techniques in automating the screening and staging of silicosis using chest X-ray images. Utilizing a comprehensive dataset, obtained from the medical records of a cohort of workers exposed to artificial quartz conglomerates, we implemented a preprocessing stage for rib-cage segmentation, followed by classification using state-of-the-art deep learning models. The segmentation model exhibited high precision, ensuring accurate identification of thoracic structures. In the screening phase, our models achieved near-perfect accuracy, with ROC AUC values reaching 1.0, effectively distinguishing between healthy individuals and those with silicosis. The models demonstrated remarkable precision in the staging of the disease. Nevertheless, differentiating between simple silicosis and progressive massive fibrosis, the evolved and complicated form of the disease, presented certain difficulties, especially during the transitional period, when assessment can be significantly subjective. Notwithstanding these difficulties, the models achieved an accuracy of around 81% and ROC AUC scores nearing 0.93. This study highlights the potential of deep learning to generate clinical decision support tools to increase the accuracy and effectiveness in the diagnosis and staging of silicosis, whose early detection would allow the patient to be moved away from all sources of occupational exposure, therefore constituting a substantial advancement in occupational health diagnostics. |
León-Jiménez, A., Rodríguez-Rubio Corona, J., Jiménez-Gómez, G. et al. High metabolic activity in positron emission tomography and systemic inflammation occurring years after exposure cessation in engineered stone silicosis. Sci Rep 15, 25364 (2025). https://doi.org/10.1038/s41598-025-10562-5
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Scientific Reports
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Engineered stone silicosis is an interstitial lung disease that progresses rapidly; in many cases, it can cause respiratory insufficiency and death. The metabolic activities occurring in the lungs and adenopathies, as well as their relationships with systemic inflammation, are unknown. Patients with complicated silicosis were enrolled in this study. All of the patients had worked for at least 5 years in finishing and installing engineered stone and had not been exposed to these working conditions for at least 7 years. Clinical data measurements, positron emission tomography/computed tomography using 18F-fluorodeoxyglucose (18F-FDG PET/CT), respiratory function tests and blood samples were performed. The mean age of the patients was 44 ± 5.4 years. Moreover, the average exposure duration was 10.94 ± 3.2 years, and the average number of years from cessation of exposure was 11.6 ± 1.6 years. The average maximum standardized uptake value (SUVmax) of large opacities was 6.32 ± 3. All of the patients demonstrated hypermetabolic mediastinal lymphadenopathies, and 88.2% of the patients also demonstrated extrathoracic lymphadenopathies. The SUVmax of the large opacities was correlated with fibrinogen (ρ = 0.717, P = 0.001), the lymphocyte-to-monocyte ratio (ρ = − 0.506, P = 0.038), the systemic inflammatory response index (ρ = 0.559, P = 0.02) and CD4+NKT cells. Large areas of lung opacity and lymphadenopathies exhibited high metabolic activities years after the cessation of silica exposure. The relationships between metabolic activity and several inflammatory factors may lead to the exploration of new therapeutic targets. |
Sanchez-Morillo, D., Martín-Carrillo, A., Priego-Torres, B., Sopo-Lambea, I., Jiménez-Gómez, G., León-Jiménez, A., & Campos-Caro, A. (2025). Cytokine Profiles as Predictive Biomarkers of Disease Severity and Progression in Engineered Stone Silicosis: A Machine Learning Approach. Diagnostics, 15(18), 2413. https://doi.org/10.3390/diagnostics15182413
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MDPI
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Background/Objectives: Silicosis caused by dust from engineered stone (ES) exposure is an emerging occupational lung disease that severely impacts respiratory health. This study aimed to analyze the association between cytokine profiles and disease severity and progression in patients with engineered stone silicosis (ESS) to assess their potential as biomarkers of progression and their usefulness to stratify risk. Methods: A longitudinal study was conducted with a seven-year follow-up (2017-2024) on 72 workers with simple silicosis (SS) or progressive massive fibrosis (PMF), all with a history of cutting, polishing, and finishing ES countertops. Data on lung function and levels of 27 cytokines were collected at four control points. Machine learning (ML) models were built to classify the disease stage and predict its progression. Results: 39% of patients with SS progressed to PMF. Significant differences in the expression of some cytokines were observed between ESS stages, suggesting a role in the evolution of the inflammatory process. Specifically, higher levels of IL-1RA, IL-8, IL-9, and IFN-γ were found at checkpoint 1 in patients with PMF compared to SS. The longitudinal analysis revealed a significant relationship between IL-1RA and MCP-1α and disease duration with MCP-1α also being associated with time and disease grade. Machine learning (ML) models were built using the cytokines selected through a sequential backward feature selection. The Support Vector Machine model achieved an accuracy of 83% in classifying disease stage (SS, PMF), and of 77% in predicting the disease progression. Conclusions: The findings suggest that cytokines can be used as dynamic biomarkers to reflect underlying inflammatory processes and monitor disease evolution. The performance of ML algorithms to predict diagnostic status based on cytokine profiles highlights their clinical value in supporting early diagnosis, monitoring disease progression, and guiding clinical decisions. |