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Malaguti MC, Gios L, Giometto B, Longo C, Riello M, Ottaviani D, Pellegrini M, Di Giacopo R, Donner D, Rozzanigo U, Chierici M, Moroni M, Jurman G, Bincoletto G, Pardini M, Bacchin R, Nobili F, Di Biasio F, Avanzino L, Marchese R, Mandich P, Garbarino S, Pagano M, Campi C, Piana M, Marenco M, Uccelli A, Osmani V. Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study. PLoS One 2024; 19:e0300127. [PMID: 38483951 PMCID: PMC10939244 DOI: 10.1371/journal.pone.0300127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 02/15/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD. OBJECTIVE The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis. METHODS The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers. RESULTS The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy). CONCLUSIONS The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.
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Affiliation(s)
| | - Lorenzo Gios
- TrentinoSalute4.0 –Competence Center for Digital Health of the Province of Trento, Trento, Italy
| | - Bruno Giometto
- Centro Interdipartimentale di Scienze Mediche (CISMed), Facoltà di Medicina e Chirurgia, Università di Trento, Trento, Italy
| | - Chiara Longo
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
| | - Marianna Riello
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
| | | | | | | | - Davide Donner
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum Università di Bologna, Bologna, Italy
| | - Umberto Rozzanigo
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
| | | | - Monica Moroni
- Fondazione Bruno Kessler Research Center, Trento, Italy
| | | | | | - Matteo Pardini
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Neuroscience, Rehabilitation, Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Ruggero Bacchin
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
| | - Flavio Nobili
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Laura Avanzino
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | | | - Paola Mandich
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- DINOGMI Department, University of Genoa, Genoa, Italy
| | | | - Mattia Pagano
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Campi
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Dipartimento Di Matematica, Università Di Genova, Genoa, Italy
| | - Michele Piana
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Dipartimento Di Matematica, Università Di Genova, Genoa, Italy
| | | | | | - Venet Osmani
- Fondazione Bruno Kessler Research Center, Trento, Italy
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Jimenez Ramos M, Kendall TJ, Drozdov I, Fallowfield JA. A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease. Ann Hepatol 2024; 29:101278. [PMID: 38135251 PMCID: PMC10907333 DOI: 10.1016/j.aohep.2023.101278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge. Artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national-level 'data commons' (SteatoSITE) as an exemplar, the opportunities, as well as the technical challenges of large-scale databases in MASLD research, are highlighted.
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Affiliation(s)
- Maria Jimenez Ramos
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK
| | - Timothy J Kendall
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK; Edinburgh Pathology, University of Edinburgh, 51 Little France Crescent, Old Dalkeith Rd, Edinburgh EH16 4SA, UK
| | - Ignat Drozdov
- Bering Limited, 54 Portland Place, London, W1B 1DY, UK
| | - Jonathan A Fallowfield
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK.
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