ADAI - Home Care in Intelligent Environments Clinical Care Ecosystem for the Management of Home Digital Therapies Through the Use of Assistive Robots and Clinical Predictive Systems Based on Artificial Intelligence Systems

Summary

Dementia is a neurocognitive disorder that causes a deterioration in cognitive function, significantly impacting social and work abilities and daily activities. Alzheimer's disease is diagnosed when cognitive decline affects at least two cognitive domains, one of which must involve memory. Mild Cognitive Impairment (MCI) is a critical diagnosis as it represents a potentially early stage of cognitive decline. In the DSM-5, MCI is defined as a "minor neurocognitive disorder," characterized by functional decline affecting at least one of six cognitive domains: memory and learning, language, visuospatial function, attention, executive function, and social functioning. It is important to emphasize that this decline is not severe enough to significantly impair the patient's daily activities. In this context, support for people with MCI and dementia is crucial, not only at the family and social level, but also through the adoption of innovative technological solutions. Artificial intelligence (AI) is emerging as a valuable tool for early diagnosis, and through machine learning processes, it is possible to predict cognitive decline, thus providing personalized treatment and day-to-day patient management. This allows for intervention at a less advanced stage of the disease, thus slowing its progression, while maintaining autonomy and independence for as long as possible, which tends to decline over time in this patient population. Investing in innovative technologies is therefore essential not only to improve prevention and treatment opportunities but also to provide concrete support to caregivers, especially at a time when the aging population requires an increasingly structured and effective global response.

The objectives of the study are as follows:

* The objective of this study is to evaluate the effectiveness of software in administering cognitive and motor tests via a humanoid robot in patients with early-stage Alzheimer's disease (AD) or other forms of mild to moderate dementia.
* Support medical professionals in personalizing therapeutic treatments, using predictive models based on advanced artificial intelligence systems. These models will begin by collecting, monitoring, and processing demographic and clinical data and the results of cognitive and motor assessments obtained from patients to predict the course of the disease and the effectiveness of rehabilitation treatments. This will then allow them to suggest personalized treatment options and optimize care pathways, thus improving overall clinical outcomes.

Conditions

• Neurodegenerative Disease
• Artificial Intelligence (AI)
• Mild Cognitive Impairment (MCI)

Interventions

DEVICE

CLINICAL INVESTIGATION WITH A MEDICAL DEVICE MARKED CE

The proposed study is an interventional study that aims to test the effectiveness of an innovative digital solution on a cohort of subjects with early-stage AD or other forms of mild to moderate dementia. Patients with early-stage Alzheimer's disease and/or other forms of dementia will be recruited from the neurology and neurodegenerative disease outpatient clinics of the IRCCS Centro Neurolesi Bonino-Pulejo in Messina. The variables that will be considered are: (i) demographic data (age, gender, education level); (ii) clinical data relating to the patient's health status, such as the presence of risk factors for neurodegenerative diseases such as hypertension, diabetes, dyslipidemia, heart disease, carotid stenosis, atrial fibrillation, and heredity and smoking; (iii) data relating to the ability to perform basic and instrumental activities of daily living and mood. The data will be recorded manually via tablet by the physician. After data collection, patients will undergo

Sponsors & Collaborators

• IRCCS Centro Neurolesi Bonino Pulejo

  lead OTHER

Study Design

Allocation

NA

Purpose

DIAGNOSTIC

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

40 Years

Max Age

80 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2025-09-22

Primary Completion

2025-10-15

Completion

2025-10-31

Countries

• Italy

Study Locations