Computational Intelligence in Healthcare

We apply Computational Intelligence methods across multiple healthcare domains to support clinicians in diagnosis, monitoring, and decision-making processes. Our work includes medical image analysis for tumor detection, machine learning models for the early identification of Parkinson’s disease, and audio signal analysis to monitor conditions such as bipolar disorder. We also investigate signal and video processing techniques for contactless patient monitoring through intelligent sensing systems. These approaches aim to provide reliable, non-invasive, and interpretable tools that assist healthcare professionals in delivering timely and personalized care.

Some recent publications

Casalino, G., Castellano, G., Pasquadibisceglie, V., & Zaza, G. (2025). Improving a mirror-based healthcare system for real-time estimation of vital parameters. Information Systems Frontiers.
Zaza, G., Casalino, G., Caputo, S., & Castellano, G. (2025). Estimating blood pressure using video-based PPG and deep learning. Image and Vision Computing.
Valerio, A. G., Trufanova, K., De Benedictis, S., Vessio, G., & Castellano, G. (2025). From segmentation to explanation: Generating textual reports from MRI with LLMs. Computer Methods and Programs in Biomedicine.
Vurro, D., Liboà, A., De Giorgio, G., Squeri, P., Liparulo, L., Zaza, G., ... & Pecori, R. (2025). Investigating the capabilities of novel silk sericin-based electrodes to measure electrocardiogram signals by using machine learning techniques. Materials Today Communications.
Casalino, G., Castellano, G., & Zaza, G. (2023). Evaluating the robustness of a contact-less mHealth solution for personal and remote monitoring of blood oxygen saturation. Journal of Ambient Intelligence and Humanized Computing.
Casalino, G., Castellano, G., & Consiglio, A. (2021). MicroRNA expression classification for pediatric multiple sclerosis identification. J Ambient Intell Human Comput.
Kaczmarek-Majer, K., Casalino, G., Castellano, G., Dominiak, M., Hryniewicz, O., Kamińska, O., ... & Díaz-Rodríguez, N. (2022). PLENARY: Explaining black-box models in natural language through fuzzy linguistic summaries. Information Sciences.
Kaczmarek-Majer, K., Casalino, G., Castellano, G., Hryniewicz, O., & Dominiak, M. (2022). Explaining smartphone-based acoustic data in bipolar disorder: Semi-supervised fuzzy clustering and relative linguistic summaries. Information Sciences.

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