@aiprognosis.bsky.social
AI-PROGNOSIS - a Horizon Europe-funded project developing Artificial intelligence-based solution for Parkinson’s disease risk assessment and prognosis. AI-PROGNOSIS receives funding from the European Union under Grant Agreement No. 101080581.
@aiprognosis.bsky.socialMargherita Fabbri (CHUT) gave a talk on early/prodromal PD as part of a session focusing on under-recognized non-motor aspects of Parkinson’s disease and how prodromal stages can vary between individuals.
@aiprognosis.bsky.socialNatalia del Campo (CHUT) presented “AI-based Progression and Medication Response Prediction in Parkinson's Disease: the AI-PMP Study Protocol”, outlining the study’s proof-of-concept AI models for PD progression and medication response prediction.
@aiprognosis.bsky.socialBjörn Falkenburger (TUD) presented “Smartwatch actigraphy for detection of REM sleep behavior disorder: the dBM-DEV study”, sharing findings on smartwatch-captured motion data for detecting REM sleep behaviour disorder under free-living conditions
@aiprognosis.bsky.socialKosmas Dimitropoulos (CERTH) presented “Smartphone video derived digital motor phenotypes of MDS UPDRS-related tasks via a comprehensive motor function test”, sharing initial results from the project’s smartphone camera-based body-tracking approach for motor assessment in PD.
@aiprognosis.bsky.social@aiprognosis.bsky.social at the 2026 #MDSCongress | Days 3–5
The final days in Seoul brought more project research, including smartphone-based motor assessment, smartwatch data, AI-based progression prediction and work on prodromal Parkinson’s disease.
More below 👇
@aiprognosis.bsky.socialIoannis Gerasimou (AUTH) presented “Differences in Sleep and Activity Digital Phenotypes from Wrist Accelerometry in Parkinson’s Disease, At-Risk Individuals, and Healthy Controls”, sharing evidence on wearable-based sleep and activity metrics for screening people at risk of PD.
@aiprognosis.bsky.socialEduardo de Pablo-Fernández (QMUL) presented “An artificial intelligence-based model to predict Parkinson’s disease risk: The AI-PRA study”, outlining the AI-PRA study and its proof-of-concept AI-based PD detection model.
@aiprognosis.bsky.social@aiprognosis.bsky.social at the 2026 #MDSCongress
Over the first two days, team members presented work on AI-based Parkinson’s disease risk prediction and wearable-derived sleep and activity metrics in people with PD, at-risk individuals and healthy controls.
More on the posters below 👇
@aiprognosis.bsky.socialThe @aiprognosis.bsky.social digital health ecosystem brings together mAI-Health, mAI-Care & mAI-Insights to support #Parkinson’s disease screening, disease management and clinical decision-making.
The applications have now matured into functional MVPs.
🔗 zenodo.org/records/2110...
@aiprognosis.bsky.social@aiprognosis.bsky.social will be at the MDS International Congress 2026 in Seoul, presenting research from the project.
📅 4–8 October
🔗 mdscongress.org
@aiprognosis.bsky.social@aiprognosis.bsky.social researcher Sara Riggare was featured in Being Patient discussing self-tracking, Parkinson’s disease and the use of generative AI.
The article also highlights AI-PROGNOSIS-supported research involving 149 people with Parkinson’s.
@aiprognosis.bsky.socialFrom chess matches to healthcare research. Over 70 years, AI has evolved from early experiments to applications across everyday life and research. In @aiprognosis.bsky.social AI is being investigated in #Parkinson’s disease research, including risk, progression and medication response.
@aiprognosis.bsky.socialHow do we build trustworthy AI in healthcare?
New white paper and insights from SmartCHANGE, @aiprognosis.bsky.social, TRUSTroke and UMBRELLA on explainability, privacy, and user-centred design.
Read 🔗https://zenodo.org/records/22658018
@aiprognosis.bsky.socialDuring an international research visit to Seoul National University Hospital, @aiprognosis.bsky.social was presented to researchers at the Human-Centered AI Lab.
Discussions focused on Parkinson’s disease, patient participation, self-care and AI-supported health technologies.
@aiprognosis.bsky.social@aiprognosis.bsky.socialA second @aiprognosis.bsky.social work presented at #EUSIPCO2026 focused on REM sleep behaviour disorder using multi-night actigraphy.
Wang et al. used wrist-acceleration data to develop a machine-learning model distinguishing people with RBD from unaffected individuals.
@aiprognosis.bsky.social@aiprognosis.bsky.socialOne of the @aiprognosis.bsky.social works presented at #EUSIPCO2026 explored touchscreen typing dynamics in Parkinson’s disease.
Gerasimou et al. used key-tap timing to compute a digital score linked to finger movement slowness.
@aiprognosis.bsky.social@aiprognosis.bsky.social is at #EUSIPCO2026 in Bruges! 🇧🇪
Tomorrow, 1 Sept, join our session “Digital Biomarkers for Chronic Diseases”, featuring two AI-PROGNOSIS works.
📍 16:00–17:40
🔗 eusipco2026.org
@aiprognosis.bsky.socialHow can predictive modelling contribute to Parkinson’s disease research?
@aiprognosis.bsky.social has published its final report, covering PD risk, progression, medication response and genetic susceptibility using Polygenic Risk Scores (PRS).
