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CORDIS - Resultados de investigaciones de la UE
CORDIS

Artificial intelligence-based Parkinson’s disease risk assessment and prognosis

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

Study initiation package (dBM-DEV study) (se abrirá en una nueva ventana)

Package including the clinical study registration number in a registry meeting WHO criteria, final version of the approved study protocol, and regulatory/ethics approvals required for the enrolment of the first study participant.

Dissemination, communication and exploitation plan (se abrirá en una nueva ventana)

The dissemination, communication and exploitation strategy (including stakeholders’ analysis), plans, language, and impact evaluation.

Initial report on domain review and datasets (se abrirá en una nueva ventana)

Initial (early R&D) report on domain review and existing, relevant health databases/sets and biobanks.

First report on predictive modelling for PD (se abrirá en una nueva ventana)

Report on early genetic profiling and early versions of predictive models for PD risk, progression, and medication response prediction, including, i.a., datasets used, methods, benchmarking, and internal validation evidence.

First report on digital biomarkers for PD (se abrirá en una nueva ventana)

Report on the early versions of dBMs for tracking key PD risk and progression markers, the associated technology, development/ implementation process, and validation or verification evidence.

Trustworthy AI development and evaluation framework (fundamental version) (se abrirá en una nueva ventana)

Fundamental compilation of guidelines for building trustworthy AI and report on relevant procedures, tools and developed metrics to be adopted for developing the AI-driven components and evaluating their performance (with emphasis on accuracy, reliability, reproducibility and generalisability).

Midterm recruitment report (dBM-DEV study) (se abrirá en una nueva ventana)

Report including an overview of the number of participants recruited by clinical sites, any recruitment problems, and a detailed description of implemented and planned measures to compensate for any incurred delays.

Initial report on user research and co-creation (se abrirá en una nueva ventana)

Initial (before R&D initiation) report on the methods, implementation and outputs of user research and co-creation processes, including empathy/journey maps and user stories/requirements.

First report on project visibility and educational material (se abrirá en una nueva ventana)

Periodic report on project visibility (dissemination, communication, and networking activities), including impact indicators, and the educational content developed.

The AI-PROGNOSIS digital health ecosystem (Alpha version) (se abrirá en una nueva ventana)

Alpha version (individual components) of the mAI-[Health, Care, Insights] apps, and companion report on features, deployment (including AI) and performance.

Project branding and communication channels (se abrirá en una nueva ventana)

Presentation of the project’s visual identity, website, social media channels and communication kit.

Publicaciones

Co-Designing a “win-win” in Predictive AI: First Results from Interviews and Focus Groups with Persons with Parkinson’s Disease (se abrirá en una nueva ventana)

Autores: Jamie Luckhaus, Sara Riggare, Anna Kharko, Charlotte Blease, Maria Hägglund, Therese Scott Duncan
Publicado en: Studies in Health Technology and Informatics, Intelligent Health Systems – From Technology to Data and Knowledge, 2025
Editor: IOS Press
DOI: 10.3233/SHTI250325

PDualNet: a deep learning framework for joint prediction of Parkinson’s disease progression subtype and MDS-UPDRS scores (se abrirá en una nueva ventana)

Autores: Vasiliki Rizou, Nikos Grammalidis, Petros Daras, Kosmas Dimitropoulos
Publicado en: Scientific Reports, Edición 15, 2025, ISSN 2045-2322
Editor: Springer Science and Business Media LLC
DOI: 10.1038/S41598-025-25812-9

Stakeholder Perspectives on Trustworthy AI for Parkinson Disease Management Using a Cocreation Approach: Qualitative Exploratory Study (se abrirá en una nueva ventana)

Autores: Beatriz Alves, Ghada Alhussein, Sara Riggare, Therese Scott Duncan, Ali Saad, David M Lyreskog, Christos Chatzichristos, Ioannis Gerasimou, Stelios Hadjidimitriou, Leontios J Hadjileontiadis, Sofia B Dias, null null
Publicado en: Journal of Medical Internet Research, Edición 27, 2025, ISSN 1438-8871
Editor: JMIR Publications Inc.
DOI: 10.2196/73710

MoveONParkinson: developing a personalized motivational solution for Parkinson’s disease management (se abrirá en una nueva ventana)

Autores: Beatriz Alves, Pedro R. Mota, Daniela Sineiro, Ricardo Carmo, Pedro Santos, Patrícia Macedo, João Casaca Carreira, Rui Neves Madeira, Sofia Balula Dias, Carla Mendes Pereira
Publicado en: Frontiers in Public Health, Edición 12, 2024, ISSN 2296-2565
Editor: Frontiers Media SA
DOI: 10.3389/fpubh.2024.1420171

Bispectral Analysis of Parkinsonian Rest Tremor: New Characterization and Classification Insights Pre-/Post-DBS and Medication Treatment (se abrirá en una nueva ventana)

Autores: Ioannis Ziogas, Charalampos Lamprou, Leontios J. Hadjileontiadis
Publicado en: IEEE Access, Edición 11, 2023, ISSN 2169-3536
Editor: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/ACCESS.2023.3324987

On capturing effects of medication change in Parkinson’s disease with wrist accelerometry-based digital biomarkers (se abrirá en una nueva ventana)

Autores: Apostolos Moustaklis, Ioannis Gerasimou, Charalampos Sotirakis, Leontios J. Hadjileontiadis, Stelios Hadjidimitriou
Publicado en: 2025 IEEE International Conference on E-health Networking, Application & Services (Healthcom), 2026
Editor: IEEE
DOI: 10.1109/HEALTHCOM60686.2025.11342793

Assessing motor skills in Parkinson's Disease using smartphone-based video analysis and machine learning (se abrirá en una nueva ventana)

Autores: Andreas Stergioulas, Sofia Dias, Beatriz Alves, Ghada Al Hussein, Sevasti Bostantjopoulou, Zoe Katsarou, Ioannis Dagklis, Nikos Grammalidis, Kosmas Dimitropoulos
Publicado en: Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments, 2024
Editor: ACM
DOI: 10.1145/3652037.3663945

An agile co-creation approach for designing a comprehensive digital motor assessment test for Parkinson’s Disease patients (se abrirá en una nueva ventana)

Autores: Alves, B., Alhussein, G., Carnide, F., Grammaliis, N., Dimitropoulos, K., Trivedi, D., Kurtis Urra, M., Hadjidimitriou, S., Hadjileontiadis, J. L., & Dias, S. B.
Editor: Zenodo
DOI: 10.5281/ZENODO.15113067

A Deep Learning Approach for Parkinsonian Tremor Assessment Using Wearables (se abrirá en una nueva ventana)

Autores: Theocharis Chatzis, Nikos Grammalidis, Kosmas Dimitropoulos
Publicado en: 2025 IEEE International Conference on E-health Networking, Application & Services (Healthcom), 2026
Editor: IEEE
DOI: 10.1109/HEALTHCOM60686.2025.11342654

Wrist Accelerometry-based Digital Assessment of Slowness of Movement in Parkinson’s Disease: a Multi-Cohort Analysis (se abrirá en una nueva ventana)

Autores: Ioannis Gerasimou, Apostolos Moustaklis, Charalampos Sotirakis, Stelios Hadjidimitriou, Leontios J. Hadjileontiadis
Publicado en: 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2025
Editor: IEEE
DOI: 10.1109/EMBC58623.2025.11252726

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