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 (RAISE_2023_026)
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.
Aperçu de l'étude
Statut
Les conditions
Intervention / Traitement
Description détaillée
Artificial intelligence (AI), particularly through machine learning techniques, offers promising opportunities in this field. By analyzing large volumes of clinical, behavioral, and demographic data, AI systems can detect patterns associated with early cognitive decline and predict disease progression. This predictive capability enables healthcare professionals to intervene earlier, when therapeutic strategies are more likely to be effective, thereby slowing the progression of the disease and prolonging the patient's independence and quality of life.
The present study aims to explore the integration of advanced technological tools into clinical practice, with a specific focus on the use of humanoid robotic systems. These systems are designed to administer standardized cognitive and motor assessments in a consistent and engaging manner, particularly for patients in the early stages of Alzheimer's disease or other forms of mild to moderate dementia. The use of a humanoid robot may enhance patient engagement, reduce variability in test administration, and allow for more precise and objective data collection.
In addition, the study seeks to support clinicians in tailoring therapeutic interventions through the use of predictive models powered by artificial intelligence. These models will be developed using comprehensive datasets that include patient demographics, medical history, and results from repeated cognitive and motor evaluations. By continuously collecting and analyzing this information, the system will be able to identify trends, estimate disease trajectories, and evaluate the effectiveness of different rehabilitation strategies.
Ultimately, the integration of AI-driven predictive analytics with robotic-assisted assessment tools aims to provide a more personalized and adaptive approach to patient care. This approach has the potential to optimize treatment plans, improve clinical outcomes, and enhance the overall efficiency of healthcare delivery. Furthermore, it offers valuable support to caregivers by providing actionable insights and facilitating more structured care pathways.
As populations continue to age globally, the demand for innovative, scalable, and effective solutions in the management of cognitive disorders is rapidly increasing. Investing in advanced technologies such as artificial intelligence and robotics is therefore crucial not only for improving early diagnosis and therapeutic interventions but also for addressing the broader societal challenges associated with dementia care.
Type d'étude
Inscription (Réel)
Phase
- N'est pas applicable
Contacts et emplacements
Lieux d'étude
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Messina
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Messina, Messina, Italie, 98123
- Irccs Centro Neurolesi Bonino Pulejo
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-
Critères de participation
Critère d'éligibilité
Âges éligibles pour étudier
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
La description
Inclusion Criteria:
- Age between 40 and 80
- Clinical Rating Scale (CDR) score < 1
- Patients with moderate to mild cognitive impairment
Exclusion Criteria:
- Subjects with marked visual and hearing impairments that prevent proper understanding of the trial
- Patients with impaired language comprehension
- Patients with comorbid psychiatric disorders
- Lack of consent to participate by signing the informed consent form
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
- Objectif principal: Diagnostique
- Répartition: N / A
- Modèle interventionnel: Affectation à un seul groupe
- Masquage: Aucun (étiquette ouverte)
Armes et Interventions
Groupe de participants / Bras |
Intervention / Traitement |
|---|---|
|
Expérimental: early-stage AD or other forms of mild to moderate dementia who interact with the robot
The study 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 neuropsychological and motor tests. |
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 |
Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
|
Mini Mental State Examination (MMSE) total score
Délai: Through study completion, an average of 1 year
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The MMSE will be administered through a humanoid robot interface. The total score (range 0-30) will be recorded, and mean scores and/or change from baseline will be analyzed. The aim of this study is therefore to evaluate the effectiveness of the software in administering MMSE via a humanoid robot in patients with early-stage Alzheimer's dementia (AD) or other forms of mild to moderate dementia. |
Through study completion, an average of 1 year
|
Collaborateurs et enquêteurs
Parrainer
Dates d'enregistrement des études
Dates principales de l'étude
Début de l'étude (Réel)
Achèvement primaire (Réel)
Achèvement de l'étude (Réel)
Dates d'inscription aux études
Première soumission
Première soumission répondant aux critères de contrôle qualité
Première publication (Réel)
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière vérification
Plus d'information
Termes liés à cette étude
Termes MeSH pertinents supplémentaires
Autres numéros d'identification d'étude
- ADAI
- AGILE srl (Autre identifiant: AGILE srl)
Plan pour les données individuelles des participants (IPD)
Prévoyez-vous de partager les données individuelles des participants (DPI) ?
Description du régime IPD
Délai de partage IPD
Critères d'accès au partage IPD
Type d'informations de prise en charge du partage d'IPD
- PROTOCOLE D'ÉTUDE
- SÈVE
- CIF
- ANALYTIC_CODE
- RSE
Informations sur les médicaments et les dispositifs, documents d'étude
Étudie un produit pharmaceutique réglementé par la FDA américaine
Étudie un produit d'appareil réglementé par la FDA américaine
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