Biosketch
After his Master’s degree in Biomedical Engineering (110/110) in 2014, Ermanno worked for one year with a collaboration contract in the Computer Systems and Bioinformatics research laboratory at the Departmental Faculty of Engineering of the University Campus Bio-medico di Roma, and then continued his studies by starting his PhD in Bioengineering and Biosciences (computer science area) at the same University in 2017, which was then completed in 2019, with a thesis entitled “Artificial intelligence meets diabetes at the micro-scale: forecasting complications and quantitatively mining insulin granules motions”.
He is currently Assistant Professor at the University Campus Bio-Medico di Roma in the Research Laboratory of Computing Systems and Bioinformatics and his main research topics are Artificial Intelligence and its applications in the health sector, Federated Learning, Computer Vision, Radiomics and IoT working within a project on the creation of a intelligent pen for diabetes treatment.
Since his PhD, Ermanno has had the opportunity to participate in international conferences and actively collaborates with other researchers to publish papers on the topics of his work. For the past three years, he has also been involved as a hackathon mentor and lecturer in the intensive training programme “Digital Tween Bootcamp” at the University Campus Bio-Medico di Roma.
Publications
For the full list of publications please follow this Scopus link. Below there is a short list of the more recent publications:
F. Prata, U. Anceschi, E. Cordelli, E. Faiella, A. Civitella, P. Tuzzolo, … & R. Papalia. (2023). Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features. Current Oncology, 30(2), 2021-2031.
V. Guarrasi, L. Tronchin, C. M. Caruso, A. Rofena, G. Manni, F. Aksu, … & P. Soda. (2023). Building an AI-enabled metaverse for intelligent healthcare: opportunities and challenges. Ital-IA 2023, (pp. 134-139)
E. Cordelli, V. Guarrasi, G. Iannello, F. Ruffini, R. Sicilia, P. Soda, L. Tronchin (2023). Making AI trustworthy in multimodal and healthcare scenarios. Ital-IA 2023, (pp. 353 – 358)
F. Conte, E. Cordelli, V. Guarrasi, G. Iannello, R. Sicilia, P. Soda, M. Tortora, L. Tronchin (2023). Sustainable AI: inside the deep, alongside the green. Ital-IA 2023, (pp. 622 – 627)
M. Tortora, E. Cordelli, R.Sicilia, L.Nibid, E.Ippolito, G.Perrone, … & P. Soda (2023). RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy. IEEE Access.
A. Calabrese, D. Santucci, M. Gravina, E. Faiella, E. Cordelli, P. Soda, G. Iannello, … & C. de Felice (2023). 3T-MRI Artificial Intelligence in Patients with Invasive Breast Cancer to Predict Distant Metastasis Status: A Pilot Study. Cancers, 15(1), 36
V. Guarrasi, N. C. D’Amico, R. Sicilia, E. Cordelli, and P. Soda (2022). Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays. Pattern Recognition, 121, 108242.
D. Santucci, E. Faiella, E. Cordelli, R. Sicilia, C. de Felice, B. B. Zobel, G. Iannello, P. Soda (2021). 3T MRI-Radiomic Approach to Predict for Lymph Node Status in Breast Cancer Patients. Cancers, 2228, 13092228.
P. Soda, N. C. D’Amico, J. Tessadori, G. Valbusa, V. Guarrasi, C. Bortolotto, M. U. Akbar, R. Sicilia, E. Cordelli, … and S. Papa (2021). AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study. Medical image analysis, 102216.
D. Santucci, E. Faiella, E. Cordelli, A. Calabrese, R. Landi, C. de Felice, … and P. Soda (2021). The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study. Cancers, 13(18), 4635.
M. Tortora, E. Cordelli, R. Sicilia, M. Miele, P. Matteucci, G. Iannello, S. Ramella and P. Soda (2021) Deep Reinforcement Learning for Fractionated Radiotherapy in Non-Small Cell Lung Carcinoma. Artificial Intelligence In Medicine.
E. Cordelli, P. Soda and G. Iannello (2021). Visual4DTracker: a tool to interact with 3D + t image stacks. BMC bioinformatics, 22(1), 1-15.
D’Amico, N. C., Sicilia, R., Cordelli, E., Tronchin, L., Greco, C., Fiore, M., … & Soda, P. (2020). Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest. Applied Sciences, 10(18), 6425.
N. C. D’Amico, M. Merone, R. Sicilia, E. Cordelli, F. D’Antoni, I. Bossi Zanetti, G. Valbusa, E. Grossi, G. Beltramo, D. Fazzini, G. Scotti, G. Iannello and P. Soda (2019) Tackling imbalance radiomics in acoustic neuroma. In International Journal of Data Mining and Bioinformatics (IJDMB).
D’Amico, N. C., Sicilia, R., Cordelli, E., Valbusa, G., Grossi, E., Zanetti, I. B., … and Soda, P. (2019). Early radiomics experiences in predicting CyberKnife response in acoustic neuroma. ACM SIGBIO Newsletter, 8(3), 11-13.
S. Ramella, M. Fiore, C. Greco, E. Cordelli, et al. (2018). A radiomic approach for adaptive radiotherapy in non-small cell lung cancer patients. In PloS one.
E. Cordelli, G. Maulucci, M. De Spirito, A. Rizzi, D. Pitocco, P. Soda (2018). A decision support system for type 1 diabetes mellitus diagnostics based on dual channel analysis of red blood cell membrane fluidity. In Computer Methods and Programs in Biomedicine (CMPB), volume 162, pages 263–271.
G. Maulucci, E. Cordelli, A. Rizzi, A. De Leba, et al. (2017). Phase separation of the plasma membrane in human red blood cells as a potential tool for diagnosis and progression monitoring of type 1 diabetes mellitus. PloS one, 2017, 12.9: e0184109.