Medicine

Medicine Research

Table of Contents


Research

Our research interest are varied and span several medicine use-cases. We have extensive experience in working with MRI images (2D and 3D), with the software used and in how to build computer vision pipelines to work on large and small image datasets. In Figure 1 you can see an example of the results of a pipeline we built (by using our internal code) to separate different parts of the brain and skull from MRI images.

We bring together different skills: medical data analysis, machine learning, statistics and software engineering. We have also an extensive network of partners that can support in different phases of research projects (for example web application development, or integration of algorithms in an extensive and existing software landscape).

Figure 1: A slice extracted from a 3D MRI scan of an adult. The different panels shows how it is possible, with our infrastructure, to separate different parts of the head anatomy (in this example skull and brain).

In Figure 2 you can see an animation of hands and where the eyes are looking from a guided reaching task done with the Kinarm exoskeleton. We developed dedicated python libraries to extract the information from proprietary files saved by the exoskeleton. The library is freely available on GitHub. The information visualised is not available out of the box in standard reports.

Figure 2: We developed dedicated python libraries to extract the information from proprietary files saved by the exoskeleton. The information visualised is not available out of the box in standard reports.

Here is a list of projects and interests that is not exhaustive.

Publications

  1. Halasz, G., Sperti, M., Villani, M., Michelucci, U., Agostoni, P., Biagi, A., Rossi, L., Botti, A., Mari, C., Maccarini, M., & others. (2021). A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score. Journal of Medical Internet Research, 23(5), e29058.
    @article{halasz2021machine,
      title = {A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score},
      author = {Halasz, Geza and Sperti, Michela and Villani, Matteo and Michelucci, Umberto and Agostoni, Piergiuseppe and Biagi, Andrea and Rossi, Luca and Botti, Andrea and Mari, Chiara and Maccarini, Marco and others},
      journal = {Journal of Medical Internet Research},
      volume = {23},
      number = {5},
      pages = {e29058},
      year = {2021},
      bibtex_show = {true},
      abbr = {Medicine},
      topic = {medicine},
      publisher = {JMIR Publications Inc., Toronto, Canada},
      link = {https://www.jmir.org/2021/5/e29058}
    }
    
  2. F.D’Ascenzo, Filippo, O. D., Gallone, G., Mittone, G., Deriu, M. A., Iannaccone, M., Ariza-Solé, A., Liebetrau, C., Manzano-Fernández, S., Quadri, G., Kinnaird, T., Campo, G., Henriques, J. S., Hughes, J., Dominguez-Rodriguez, A., Aldinucci, M., Morbiducci, U., Patti, G., Raposeiras-Roubin, S., … Arfat, Y. (2021). Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. The Lancet, 397(10270), 199–207.
    @article{2021machine,
      title = {Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets},
      author = {F.D'Ascenzo and Filippo, O. De and Gallone, G. and Mittone, G. and Deriu, M.A. and Iannaccone, M. and Ariza-Solé, A. and Liebetrau, C. and Manzano-Fernández, S. and Quadri, G. and Kinnaird, T. and Campo, G. and Henriques, J. S. and Hughes, J. and Dominguez-Rodriguez, A. and Aldinucci, M. and Morbiducci, U. and Patti, G. and Raposeiras-Roubin, S. and Abu-Assi, E. and Ferrari, G.M. De and Piroli, F. and Saglietto, A. and Conrotto, F. and Omedé, P. and Montefusco, A. and Pennone, M. and Bruno, F. and Bocchino, P. Paolo and Boccuzzi, G. and Cerrato, E. and F. and Varbella, O. and Sperti, M. and Wilton, S. B. and Velicki, L. and Xanthopoulou, I. and Cequier, A. and Iniguez-Romo, A. and Pousa, I. Munoz and Fern, M. Cespon and Queija, B. Caneiro and Cobas-Paz, R. and Lopez-Cuenca, A. and Garay, A. and Blanco, P. Flores and Rognoni, A. and Zoccai, G. Biondi and Biscaglia, S. and Nunez-Gil, I. and Fujii, T. and A. and ro Durante and Song, X. and Kawaji, T. and Alexopoulos, D. and Huczek, Z. and Juanatey, J. R. G. and Nie, S.-P. and Kawashiri, M.-aki and Colonnelli, I. and Cantalupo, B. and Esposito, R. and Leonardi, S. and Marra, W. G. and Chieffo, A. and Michelucci, U. and Piga, D. and Malavolta, M. and Gili, S. and Mennuni, M. and Montalto, C. and Visconti, L. O. and Arfat, Y.},
      journal = {The Lancet},
      volume = {397},
      number = {10270},
      pages = {199--207},
      year = {2021},
      bibtex_show = {true},
      abbr = {Medicine},
      topic = {medicine},
      link = {https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)32519-8/fulltext}
    }
    

References cited in this page

  1. F.D’Ascenzo, Filippo, O. D., Gallone, G., Mittone, G., Deriu, M. A., Iannaccone, M., Ariza-Solé, A., Liebetrau, C., Manzano-Fernández, S., Quadri, G., Kinnaird, T., Campo, G., Henriques, J. S., Hughes, J., Dominguez-Rodriguez, A., Aldinucci, M., Morbiducci, U., Patti, G., Raposeiras-Roubin, S., … Arfat, Y. (2021). Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. The Lancet, 397(10270), 199–207.
    @article{2021machine,
      title = {Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets},
      author = {F.D'Ascenzo and Filippo, O. De and Gallone, G. and Mittone, G. and Deriu, M.A. and Iannaccone, M. and Ariza-Solé, A. and Liebetrau, C. and Manzano-Fernández, S. and Quadri, G. and Kinnaird, T. and Campo, G. and Henriques, J. S. and Hughes, J. and Dominguez-Rodriguez, A. and Aldinucci, M. and Morbiducci, U. and Patti, G. and Raposeiras-Roubin, S. and Abu-Assi, E. and Ferrari, G.M. De and Piroli, F. and Saglietto, A. and Conrotto, F. and Omedé, P. and Montefusco, A. and Pennone, M. and Bruno, F. and Bocchino, P. Paolo and Boccuzzi, G. and Cerrato, E. and F. and Varbella, O. and Sperti, M. and Wilton, S. B. and Velicki, L. and Xanthopoulou, I. and Cequier, A. and Iniguez-Romo, A. and Pousa, I. Munoz and Fern, M. Cespon and Queija, B. Caneiro and Cobas-Paz, R. and Lopez-Cuenca, A. and Garay, A. and Blanco, P. Flores and Rognoni, A. and Zoccai, G. Biondi and Biscaglia, S. and Nunez-Gil, I. and Fujii, T. and A. and ro Durante and Song, X. and Kawaji, T. and Alexopoulos, D. and Huczek, Z. and Juanatey, J. R. G. and Nie, S.-P. and Kawashiri, M.-aki and Colonnelli, I. and Cantalupo, B. and Esposito, R. and Leonardi, S. and Marra, W. G. and Chieffo, A. and Michelucci, U. and Piga, D. and Malavolta, M. and Gili, S. and Mennuni, M. and Montalto, C. and Visconti, L. O. and Arfat, Y.},
      journal = {The Lancet},
      volume = {397},
      number = {10270},
      pages = {199--207},
      year = {2021},
      bibtex_show = {true},
      abbr = {Medicine},
      topic = {medicine},
      link = {https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)32519-8/fulltext}
    }
    

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