AI-PROGNOSIS_D5.5 / Study initiation package (AI-PMP study)

Executive summary D5.5 “Study Initiation Package (AI-PMP Study)” provides the finalised documentation required for the set-up, initiation, and compliant conduct of the AI-PMP clinical study, including the study protocol, the informed consent form to be signed by participants, the Standard Operating Procedures (SOPs), the laboratory manual, and the GDPR-compliant data management infrastructure. It also outlines the OpenClinica electronic clinical data capture system, lists the study registration on ClinicalTrials.gov (NCT06444789), and includes the ethics approval status across sites, along with the letters from the respective local ethics committees (with submission pending at the last of the four sites, QMUL in the UK). The AI-PMP study aims to establish proof-of-concept for two AI-driven multivariable prognostic models for disease progression and medication response, integrating clinical, demographic, lifestyle, genetic, and digital biomarker (dBM) data, to enhance clinical decision-making in Parkinson’s disease (PD). Model development leveraged existing databases and biobanks and their operationalisation is supported by two novel digital health tools developed within AI-PROGNOSIS, the mAI-Care mobile app for patient self-logging and smartwatch data capturing, and the mAI-Insights platform for clinician data entry and data-driven insights. Importantly, the AI-PMP study serves as a translational bridge between predictive modelling and clinical validation, supporting the broader mission of AI-PROGNOSIS to deliver personalized, data-driven care pathways for PD.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-01
DOI
https://doi.org/10.5281/zenodo.21104239
Primary Topic
Parkinson's Disease Mechanisms and Treatments
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-PROGNOSIS_D5.5 / Study initiation package (AI-PMP study)

Natalia del Campo
Zenodo (CERN European Organization for Nuclear Research)
Parkinson's Disease Mechanisms and Treatments
article

AI-PROGNOSIS_D5.5 / Study initiation package (AI-PMP study)

Natalia del Campo
article en

Abstract

Executive summary D5.5 “Study Initiation Package (AI-PMP Study)” provides the finalised documentation required for the set-up, initiation, and compliant conduct of the AI-PMP clinical study, including the study protocol, the informed consent form to be signed by participants, the Standard Operating Procedures (SOPs), the laboratory manual, and the GDPR-compliant data management infrastructure. It also outlines the OpenClinica electronic clinical data capture system, lists the study registration on ClinicalTrials.gov (NCT06444789), and includes the ethics approval status across sites, along with the letters from the respective local ethics committees (with submission pending at the last of the four sites, QMUL in the UK). The AI-PMP study aims to establish proof-of-concept for two AI-driven multivariable prognostic models for disease progression and medication response, integrating clinical, demographic, lifestyle, genetic, and digital biomarker (dBM) data, to enhance clinical decision-making in Parkinson’s disease (PD). Model development leveraged existing databases and biobanks and their operationalisation is supported by two novel digital health tools developed within AI-PROGNOSIS, the mAI-Care mobile app for patient self-logging and smartwatch data capturing, and the mAI-Insights platform for clinician data entry and data-driven insights. Importantly, the AI-PMP study serves as a translational bridge between predictive modelling and clinical validation, supporting the broader mission of AI-PROGNOSIS to deliver personalized, data-driven care pathways for PD.

Zenodo (CERN European Organization for Nuclear Research)
European Commission
Industry, innovation and infrastructure
Openalex Percentile: Top 7%
Parkinson's Disease Mechanisms and Treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.