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.
調査の概要
状態
詳細な説明
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.
研究の種類
入学 (実際)
段階
- 適用できない
連絡先と場所
研究場所
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-
Messina
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Messina、Messina、イタリア、98123
- Irccs Centro Neurolesi Bonino Pulejo
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参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
説明
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
研究計画
研究はどのように設計されていますか？
デザインの詳細
- 主な目的：診断
- 割り当て：なし
- 介入モデル：単一グループの割り当て
- マスキング：なし（オープンラベル）
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
|
実験的：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 |
この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Mini Mental State Examination (MMSE) total score
時間枠：Through study completion, an average of 1 year
|
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
|
協力者と研究者
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (実際)
研究の完了 (実際)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
その他の研究ID番号
- ADAI
- AGILE srl (その他の識別子：AGILE srl)
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
IPD プランの説明
IPD 共有時間枠
IPD 共有アクセス基準
IPD 共有サポート情報タイプ
- STUDY_PROTOCOL
- SAP
- ICF
- ANALYTIC_CODE
- CSR
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
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