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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, 2024-03, Vol.19 (3), p.e0300127-e0300127 [Peer Reviewed Journal]

Copyright: © 2024 Malaguti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ;COPYRIGHT 2024 Public Library of Science ;2024 Malaguti et al 2024 Malaguti et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0300127 ;PMID: 38483951

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Title:
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
Author: Malaguti, Maria Chiara ; Gios, Lorenzo ; Giometto, Bruno ; Longo, Chiara ; Riello, Marianna ; Ottaviani, Donatella ; Pellegrini, Maria ; Di Giacopo, Raffaella ; Donner, Davide ; Rozzanigo, Umberto ; Chierici, Marco ; Moroni, Monica ; Jurman, Giuseppe ; Bincoletto, Giorgia ; Pardini, Matteo ; Bacchin, Ruggero ; Nobili, Flavio ; Di Biasio, Francesca ; Avanzino, Laura ; Marchese, Roberta ; Mandich, Paola ; Garbarino, Sara ; Pagano, Mattia ; Campi, Cristina ; Piana, Michele ; Marenco, Manuela ; Uccelli, Antonio ; Osmani, Venet
Subjects: Artificial Intelligence ; Biology and Life Sciences ; Care and treatment ; Computer and Information Sciences ; Diagnosis ; Forecasts and trends ; Health care industry ; Humans ; Innovations ; Medicine and Health Sciences ; Multicenter Studies as Topic ; Observational Studies as Topic ; Parkinson Disease - diagnosis ; Parkinson's disease ; People and places ; Physical Sciences ; Prospective Studies ; Public Health ; Research and Analysis Methods ; Retrospective Studies ; Study Protocol ; Technology application
Is Part Of: PloS one, 2024-03, Vol.19 (3), p.e0300127-e0300127
Description: 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. 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. 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. 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). 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.
Publisher: United States: Public Library of Science
Language: English
Identifier: ISSN: 1932-6203
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0300127
PMID: 38483951
Source: TestCollectionTL3OpenAccess
GFMER Free Medical Journals
MEDLINE
PubMed Central
Public Library of Science (PLoS)
ProQuest Central

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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

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