| [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. |
IEEE
RODIN |
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 |
WILEY
RODIN |
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. |
