AI-based predictive biomarkers for chronic neurological diseases: the rAIdD prospective, multicenter, observational study protocol - Scorecard - MDSpire

Predictive Biomarkers Utilizing AI for Chronic Neurological Disorders: Protocol for the rAIdD Multicenter Observational Study

  • By

  • Simone Varrasi

  • Alfredo Pulvirenti

  • Vincenzo Catania

  • Maurizio Palesi

  • Concetto Spampinato

  • Davide Patti

  • Orazio Tomarchio

  • Giovanni Micale

  • Alessia Simone

  • Lisa Passarello

  • Federica Proietto Salanitri

  • Giovanni Patanè

  • Salvatore Ravidà

  • Clara Grazia Chisari

  • Giuseppe Zappalà

  • Emanuele D'Amico

  • Carlo Avolio

  • Federica Felicetti

  • Claudio Gasperini

  • Simone Rossi

  • Paolo Manganotti

  • Pierpaolo Busan

  • Carmelo Rodolico

  • Rossella Laudani

  • Roberto Marino

  • Massimo Villari

  • Francesco Patti

  • July 17, 2026

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Clinical Scorecard: Predictive Biomarkers Utilizing AI for Chronic Neurological Disorders: Protocol for the rAIdD Multicenter Observational Study

At a Glance

CategoryDetail
ConditionChronic Neurological Disorders
Key MechanismsNeuroinflammation and neurodegeneration influenced by genetic, environmental, and lifestyle factors.
Target PopulationPatients with Multiple Sclerosis, Parkinson's Disease, and Alzheimer's Disease.
Care SettingMulticenter observational study across academic and clinical centers.

Key Highlights

  • Study involves 780 participants: 300 MS, 150 PD, 150 AD, and 180 healthy controls.
  • Focus on identifying predictive digital biomarkers using AI and wearable technologies.
  • Longitudinal assessments include clinical, neuropsychological, and neuroimaging data.
  • Continuous digital monitoring of biometric and behavioral data through wearable sensors.
  • Results will support knowledge translation and implementation of precision neurology.

Guideline-Based Recommendations

Diagnosis

  • Utilize standardized clinical and neuropsychological assessments.

Management

  • Implement personalized disease management strategies based on predictive biomarkers.

Monitoring & Follow-up

  • Conduct continuous digital monitoring using wearable sensors.

Risks

  • Consider multifactorial influences on disease progression and outcomes.

Patient & Prescribing Data

Individuals diagnosed with MS, PD, and AD.

Integration of digital measures with clinical assessments for personalized interventions.

Clinical Best Practices

  • Adopt a multidisciplinary approach for chronic neurological disorders.
  • Incorporate AI and big-data analytics in disease management.
  • Utilize wearable technologies for continuous patient monitoring.

Related Resources & Content

    Original Source(s)

    AI-based predictive biomarkers for chronic neurological diseases: the rAIdD prospective, multicenter, observational study protocol

    • by Simone Varrasi, Alfredo Pulvirenti, Vincenzo Catania, Maurizio Palesi, Concetto Spampinato, Davide Patti, Orazio Tomarchio, Giovanni Micale, Alessia Simone, Lisa Passarello, Federica Proietto Salanitri, Giovanni Patanè, Salvatore Ravidà, Clara Grazia Chisari, Giuseppe Zappalà, Emanuele D'Amico, Carlo Avolio, Federica Felicetti, Claudio Gasperini, Simone Rossi, Paolo Manganotti, Pierpaolo Busan, Carmelo Rodolico, Rossella Laudani, Roberto Marino, Massimo Villari, Francesco Patti

    • July 17, 2026

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