Artificial Intelligence–Enhanced 3D Reconstruction of Parapneumonic Effusion in a Pediatric Patient: A Novel Visualization Approach

Authors

  • Gabriel Román-Ríos Department of Pediatrics and Basic Science, Ponce Health Sciences University, Ponce, PR Author
  • Arnaldo Santos-López Department of Pediatrics, St. Lukes Episcopal Medical Center, Ponce, PR Author
  • Dr. De Jesús Rojas Department of Pediatrics and Basic Science, Ponce Health Sciences University, Ponce, PR Author

DOI:

https://doi.org/10.71332/kg6svc69

Keywords:

pediatric pneumonia, pleural effusion, artificial intelligence, 3D lung reconstruction, computed tomography, lung segmentation, parapneumonic effusion, innovative imaging

Abstract

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References

1. Fedorov, A.; Beichel, R.; Kalpathy-Cramer, J.; Finet, J.; Fillion-Robin, J.-C.; Pujol, S.; Bauer, C.; Jennings, D.; Fennessy, F.; Sonka, M.; et al. 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magn Reson Imaging 2012, 30, 1323–1341, doi:10.1016/j.mri.2012.05.001.

2. Bumm, R.; Lasso, A.; Kawel-Böhm, N.; Wäckerlin, A.; Ludwig, P.; Furrer, M. First Results of Spatial Reconstruction and Quantification of COVID-19 Chest CT Infiltrates Using Lung CT Analyzer and 3D Slicer. British Journal of Surgery 2021, 108, znab202.077, doi:10.1093/bjs/znab202.077.

3. Marchi, G.; Mercier, M.; Cefalo, J.; Salerni, C.; Ferioli, M.; Candoli, P.; Gori, L.; Cucchiara, F.; Cenerini, G.; Guglielmi, G.; et al. Advanced Imaging Techniques and Artificial Intelligence in Pleural Diseases: A Narrative Review. Eur Respir Rev 2025, 34, 240263, doi:10.1183/16000617.0263-2024.

4. Hu, K.; Chopra, A.; Kurman, J.; Huggins, J.T. Management of Complex Pleural Disease in the Critically Ill Patient. J Thorac Dis 2021, 13, 5205–5222, doi:10.21037/jtd-2021-31.

5. Khalifa, M.; Albadawy, M. AI in Diagnostic Imaging: Revolutionising Accuracy and Efficiency. Computer Methods and Programs in Biomedicine Update 2024, 5, 100146, doi:10.1016/j.cmpbup.2024.100146.

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Published

2026-02-27