Alzheimer's Disease (AD)AILANNCT07906769
Automated Language Analysis in Neurodegenerative Diseases: Application of Artificial Intelligence Models
A study testing Speech and language Test to support clinical diagnosis in people with Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD), run by Istituti Clinici Scientifici Maugeri SpA at 1 site in Italy.
Trial summary
This multicenter study aims to characterize the language profiles of patients with various neurodegenerative diseases, including Alzheimer's disease (AD), Primary Progressive Aphasia (PPA), behavioral variant Frontotemporal Dementia (bvFTD), Dementia with Lewy Bodies (DLB), and Parkinson's disease (PD), using… Read more about this study
- Who is it for?
- Ages 40 to 90 yearsHealthy volunteers can take part
- Conditions the study is for
- Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), Parkinsons Disease (PD)
- What is being tested?
- Speech and language Test to support clinical diagnosis
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 200 participants
- Where
- 1 siteItaly
- Study dates
- 20 Oct 2025to 31 Oct 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 90 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Diagnosis of mild cognitive impairment due to Alzheimer's disease (AD), primary progressive aphasia (PPA), behavioral variant frontotemporal dementia (bvFTD), dementia with Lewy bodies (DLB), or Parkinson's disease (PD).
- Age 40-85 years.
- Italian native speaker.
- Able to provide informed consent.
- Italian native speaker.
- Matched to the patient groups for age, sex, and education level.
Who cannot join
- Mini-Mental State Examination (MMSE) score below 15.
- Other neurological or psychiatric diagnoses, including cerebrovascular disease documented by MRI or CT.
- Severe, uncorrected hearing or visual impairment.
- Clinical Dementia Rating (CDR) score above 1.
- Healthy controls
- History of systemic, neurological, or psychiatric disease.
Show all 9Show fewer
- Substance abuse.
- Family history of neurodegenerative disease.
- MMSE score below 27/30.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Gaia C Santi, PhD contact
Italy1 site, 1 recruiting
- Recruiting
Istituti Clinici Scientifici Maugeri IRCCS Bari
Bari, 70124
Petronilla Battista, PhD, petronilla.battista@icsmaugeri.it
Simona Aresta, simona.aresta@icsmaugeri.it
About this study
Official title: Analisi Automatizzata Del Linguaggio Nelle Malattie Neurodegenerative: Applicazione di Modelli di Intelligenza Artificiale
This multicenter study aims to characterize the language profiles of patients with various neurodegenerative diseases, including Alzheimer's disease (AD), Primary Progressive Aphasia (PPA), behavioral variant Frontotemporal Dementia (bvFTD), Dementia with Lewy Bodies (DLB), and Parkinson's disease (PD), using artificial intelligence (AI) models. By analyzing speech and language data collected during remote neuropsychological assessments, the researchers intend to identify objective linguistic biomarkers suitable for early and differential diagnosis, overcoming the limitations of traditional, invasive, and costly diagnostic tools. The project will develop and validate an AI-driven pipeline to provide scalable and individualized linguistic profiles, ultimately supporting personalized treatment strategies and improving clinical workflow efficiency in the Italian population.
Read the detailed description
Neurodegenerative disorders pose significant diagnostic challenges, particularly in differentiating conditions with overlapping clinical symptoms and neuroanatomy. Although language impairment is a hallmark symptom across several forms of dementia, existing diagnostic tools are often costly, time-consuming, and lack cultural sensitivity, particularly for the Italian language. Artificial Intelligence (AI) offers a promising, non-invasive approach to extracting objective acoustic, prosodic, and linguistic markers from speech data.
The primary objective of this study is to characterize the language profiles of patients diagnosed with AD, PPA, bvFTD, DLB, and PD compared to healthy controls (HC). Specific objectives include: 1) Identifying distinct linguistic and speech features that act as surrogate biomarkers for early and differential diagnosis; 2) Using machine learning to optimize classification algorithms; and 3) Implementing Explainable AI (XAI) to ensure the interpretability of diagnostic outputs, thereby facilitating clinical trust and adoption of AI-driven tools in the diagnostic process.
This is a prospective, experimental, multicenter study. A total of 168 participants (24 per group: AD, PPA, bvFTD, DLB, PD, and HC) will be enrolled. Participants will undergo a 1.5-hour remote neuropsychological and logopedic evaluation via a secure, HIPAA-compliant videoconferencing platform. The assessment includes spontaneous speech tasks (e.g., image descriptions) and standardized language and cognitive tests. Audio data will be recorded and processed using an AI pipeline, including automatic speech recognition for transcription and various Natural Language Processing (NLP) techniques-such as BERT, RoBERTa, and UmBERTo-to extract complex linguistic and semantic features.
Data analysis will involve training machine learning classifiers (e.g., Support Vector Machines, Random Forests) to distinguish between diagnostic groups, utilizing nested cross-validation to ensure robustness. XAI techniques, including SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations), will be applied to enhance the transparency of the model's decisions, allowing clinicians to interpret the pathological language patterns driving the classification. The study aims to establish the first structured Italian corpus for the linguistic characterization of neurodegenerative disorders, providing a foundation for future cross-linguistic analysis.
- Conditions
- Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), Parkinsons Disease (PD), Lewy Body Dementia
- Being tested
- Speech and language Test to support clinical diagnosis
- Keywords
- Neurodegenerative diseases, Automated Speech and language analysis, Artificial intelligence (machine learning)
- Medical subject headings
- Alzheimer Disease, Frontotemporal Dementia, Parkinson Disease, Lewy Body Disease, Neurodegenerative Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Speech and language Test to support clinical diagnosisBehavioural
Participants will complete a one-time remote speech and language assessment. Speech and language samples will be collected and analyzed using artificial intelligence and machine-learning methods. No therapeutic intervention will be administered.
| Study group | Role | What they receive |
|---|---|---|
| Healthy Controls | Active comparator | Healthy control participants will complete a one-time remote speech and language assessment. No therapeutic intervention will be administered. Receives: Behavioral: Speech and language Test to support clinical diagnosis |
| Lewy bodies or Parkinson's | Experimental | Participants with dementia with Lewy bodies or Parkinson's disease will complete a one-time remote speech and language assessment. No therapeutic intervention will be administered. Receives: Behavioral: Speech and language Test to support clinical diagnosis |
| frontotemporal dementia | Experimental | Participants with frontotemporal dementia will complete a one-time remote speech and language assessment. No therapeutic intervention will be administered. Receives: Behavioral: Speech and language Test to support clinical diagnosis |
| Alzheimer's Disease (AD) | Experimental | Participants with Alzheimer's disease will complete a one-time remote speech and language assessment. No therapeutic intervention will be administered. Receives: Behavioral: Speech and language Test to support clinical diagnosis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Performance of AI-based language analysis in distinguishing diagnostic groups
Accuracy of machine-learning models in classifying participants into the study groups (Alzheimer's disease, primary progressive aphasia, behavioral variant frontotemporal dementia, dementia with Lewy bodies, Parkinson's disease, and healthy controls) based on speech and language features from the remote assessment.
Measured: Day 1 (At completion of the one-time remote speech and language assessment and analysis.)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Oct 2026
- Study start20 Oct 2025
- Primary completion31 Jan 2028 (estimated)
- Study completion31 Oct 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Alzheimer's Disease (AD)Studies of the same condition, open and under way.
- More trials run by Istituti Clinici Scientifici Maugeri SpAThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Single group
- Model description
- This is a prospective, multicenter, single-group study with parallel diagnostic groups. Participants will undergo one remote neuropsychological and speech-language assessment; no therapeutic intervention is administered. Speech and language recordings will be analyzed using artificial intelligence and machine-learning methods to characterize language profiles and explore classification across diagnostic groups and healthy controls.
- Masking
- None (open label)
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Istituti Clinici Scientifici Maugeri SpA
- Sponsor type
- University, hospital or other
- Collaborators
- Università degli studi di Trieste, IRCCS Policlinico S. Donato
- Responsible party
- Sponsor
- Investigators
- Petronilla Battista, PhD, Principal investigator, Istituti Clinici Scientifici Maugeri IRCCS BariGaia Santi, PhD, Study chair, Istituti Clinici Scientifici Maugeri IRCCS LumezzaneSimona Aresta, PhD, Study director, Istituti Clinici Scientifici Maugeri IRCCS Bari
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07906769
- Sponsor's protocol ID
- Prot.2541/CEL
- Acronym
- AILAN
- Status last verified
- Oct 2026
- First submitted
- 26 Oct 2026
- Last updated
- 3 Nov 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared because the speech and language recordings and related clinical information are sensitive and potentially identifiable. The study team will protect participant confidentiality and use the data only for the purposes described in the protocol.
Publications
Battista P, Salvatore C, Berlingeri M, Cerasa A, Castiglioni I. Artificial intelligence and neuropsychological measures: The case of Alzheimer's disease. Neurosci Biobehav Rev. 2020 Jul;114:211-228. doi: 10.1016/j.neubiorev.2020.04.026. Epub 2020 May 11.
PubMed 32437744Palmirotta C, Aresta S, Battista P, Tagliente S, Lagravinese G, Mongelli D, Gelao C, Fiore P, Castiglioni I, Minafra B, Salvatore C. Unveiling the Diagnostic Potential of Linguistic Markers in Identifying Individuals with Parkinson's Disease through Artificial Intelligence: A Systematic Review. Brain Sci. 2024 Jan 28;14(2):137. doi: 10.3390/brainsci14020137.
PubMed 38391712Battista, P., Aresta, S., Santacesaria, P., Benzini, A., Palmirotta, C., Tagliente, S., ... & Benussi, A. (2025). Natural Language Processing differentiates between Italian individuals with Nonfluent/Agrammatic from Logopenic variants of PPA. Alzheimer's & Dementia, 21, e105208.
Aresta S, Battista P, Palmirotta C, Tagliente S, Lagravinese G, Santacesaria P, Benzini A, Mongelli D, Minafra B, Lunetta C, Garcia AM, Salvatore C. Digital phenotyping of Parkinson's disease via natural language processing. NPJ Parkinsons Dis. 2025 Jun 23;11(1):182. doi: 10.1038/s41531-025-01050-8.
PubMed 40550806
Study at a glance
From the registry record, last updated by the sponsor on 3 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Analisi Automatizzata Del Linguaggio Nelle Malattie Neurodegenerative: Applicazione di Modelli di Intelligenza Artificiale
- ClinicalTrials.gov ID
- NCT07906769
- Acronym / sponsor ID
- AILAN / Prot.2541/CEL
- Conditions
- Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), Parkinsons Disease (PD), Lewy Body Dementia
- Keywords
- Neurodegenerative diseases, Automated Speech and language analysis, Artificial intelligence (machine learning)
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Istituti Clinici Scientifici Maugeri SpAUniversity, hospital or other
- Collaborators
- Università degli studi di Trieste, IRCCS Policlinico S. Donato
- Study start
- 20 Oct 2025 (actual)
- Primary completion
- 31 Jan 2028 (estimated)
- Study completion
- 31 Oct 2028 (estimated)
- First posted
- 30 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This multicenter study aims to characterize the language profiles of patients with various neurodegenerative diseases, including Alzheimer's disease (AD), Primary Progressive Aphasia (PPA), behavioral variant Frontotemporal Dementia (bvFTD), Dementia with Lewy Bodies (DLB), and Parkinson's disease (PD), using artificial intelligence (AI) models. By analyzing speech and language data collected during remote neuropsychological assessments, the researchers intend to identify objective linguistic biomarkers suitable for early and differential diagnosis, overcoming the limitations of traditional, invasive, and costly diagnostic tools. The project will develop and validate an AI-driven pipeline to provide scalable and individualized linguistic profiles, ultimately supporting personalized treatment strategies and improving clinical workflow efficiency in the Italian population.
Detailed description
Neurodegenerative disorders pose significant diagnostic challenges, particularly in differentiating conditions with overlapping clinical symptoms and neuroanatomy. Although language impairment is a hallmark symptom across several forms of dementia, existing diagnostic tools are often costly, time-consuming, and lack cultural sensitivity, particularly for the Italian language. Artificial Intelligence (AI) offers a promising, non-invasive approach to extracting objective acoustic, prosodic, and linguistic markers from speech data.
The primary objective of this study is to characterize the language profiles of patients diagnosed with AD, PPA, bvFTD, DLB, and PD compared to healthy controls (HC). Specific objectives include: 1) Identifying distinct linguistic and speech features that act as surrogate biomarkers for early and differential diagnosis; 2) Using machine learning to optimize classification algorithms; and 3) Implementing Explainable AI (XAI) to ensure the interpretability of diagnostic outputs, thereby facilitating clinical trust and adoption of AI-driven tools in the diagnostic process.
This is a prospective, experimental, multicenter study. A total of 168 participants (24 per group: AD, PPA, bvFTD, DLB, PD, and HC) will be enrolled. Participants will undergo a 1.5-hour remote neuropsychological and logopedic evaluation via a secure, HIPAA-compliant videoconferencing platform. The assessment includes spontaneous speech tasks (e.g., image descriptions) and standardized language and cognitive tests. Audio data will be recorded and processed using an AI pipeline, including automatic speech recognition for transcription and various Natural Language Processing (NLP) techniques-such as BERT, RoBERTa, and UmBERTo-to extract complex linguistic and semantic features.
Data analysis will involve training machine learning classifiers (e.g., Support Vector Machines, Random Forests) to distinguish between diagnostic groups, utilizing nested cross-validation to ensure robustness. XAI techniques, including SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations), will be applied to enhance the transparency of the model's decisions, allowing clinicians to interpret the pathological language patterns driving the classification. The study aims to establish the first structured Italian corpus for the linguistic characterization of neurodegenerative disorders, providing a foundation for future cross-linguistic analysis.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 4
- Model description
- This is a prospective, multicenter, single-group study with parallel diagnostic groups. Participants will undergo one remote neuropsychological and speech-language assessment; no therapeutic intervention is administered. Speech and language recordings will be analyzed using artificial intelligence and machine-learning methods to characterize language profiles and explore classification across diagnostic groups and healthy controls.
| Arm | Type | Receives |
|---|---|---|
| Healthy Controls | Active comparator | Behavioral: Speech and language Test to support clinical diagnosisHealthy control participants will complete a one-time remote speech and language assessment. No therapeutic intervention will be administered. |
| Lewy bodies or Parkinson's | Experimental | Behavioral: Speech and language Test to support clinical diagnosisParticipants with dementia with Lewy bodies or Parkinson's disease will complete a one-time remote speech and language assessment. No therapeutic intervention will be administered. |
| frontotemporal dementia | Experimental | Behavioral: Speech and language Test to support clinical diagnosisParticipants with frontotemporal dementia will complete a one-time remote speech and language assessment. No therapeutic intervention will be administered. |
| Alzheimer's Disease (AD) | Experimental | Behavioral: Speech and language Test to support clinical diagnosisParticipants with Alzheimer's disease will complete a one-time remote speech and language assessment. No therapeutic intervention will be administered. |
Who may qualify
- Ages
- 40 to 90 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- Diagnosis of mild cognitive impairment due to Alzheimer's disease (AD), primary progressive aphasia (PPA), behavioral variant frontotemporal dementia (bvFTD), dementia with Lewy bodies (DLB), or Parkinson's disease (PD).
- Age 40-85 years.
- Italian native speaker.
- Able to provide informed consent.
- Italian native speaker.
Key exclusion criteria (9 listed)
- Mini-Mental State Examination (MMSE) score below 15.
- Other neurological or psychiatric diagnoses, including cerebrovascular disease documented by MRI or CT.
- Severe, uncorrected hearing or visual impairment.
- Clinical Dementia Rating (CDR) score above 1.
- Healthy controls
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Speech and language Test to support clinical diagnosisParticipants will complete a one-time remote speech and language assessment. Speech and language samples will be collected and analyzed using artificial intelligence and machine-learning methods. No therapeutic intervention will be administered. | Behavioural | Alzheimer's Disease (AD), Healthy Controls, Lewy bodies or Parkinson's, frontotemporal dementia |
Primary outcome
Performance of AI-based language analysis in distinguishing diagnostic groups
Time frame: Day 1 (At completion of the one-time remote speech and language assessment and analysis.)
Accuracy of machine-learning models in classifying participants into the study groups (Alzheimer's disease, primary progressive aphasia, behavioral variant frontotemporal dementia, dementia with Lewy bodies, Parkinson's disease, and healthy controls) based on speech and language features from the remote assessment.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Nov 2026.
- Background: PubMed 32437744Battista P, Salvatore C, Berlingeri M, Cerasa A, Castiglioni I. Artificial intelligence and neuropsychological measures: The case of Alzheimer's disease. Neurosci Biobehav Rev. 2020 Jul;114:211-228. doi: 10.1016/j.neubiorev.2020.04.026. Epub 2020 May 11.
- Background: PubMed 38391712Palmirotta C, Aresta S, Battista P, Tagliente S, Lagravinese G, Mongelli D, Gelao C, Fiore P, Castiglioni I, Minafra B, Salvatore C. Unveiling the Diagnostic Potential of Linguistic Markers in Identifying Individuals with Parkinson's Disease through Artificial Intelligence: A Systematic Review. Brain Sci. 2024 Jan 28;14(2):137. doi: 10.3390/brainsci14020137.
- Results publicationBattista, P., Aresta, S., Santacesaria, P., Benzini, A., Palmirotta, C., Tagliente, S., ... & Benussi, A. (2025). Natural Language Processing differentiates between Italian individuals with Nonfluent/Agrammatic from Logopenic variants of PPA. Alzheimer's & Dementia, 21, e105208.
- Results publication: PubMed 40550806Aresta S, Battista P, Palmirotta C, Tagliente S, Lagravinese G, Santacesaria P, Benzini A, Mongelli D, Minafra B, Lunetta C, Garcia AM, Salvatore C. Digital phenotyping of Parkinson's disease via natural language processing. NPJ Parkinsons Dis. 2025 Jun 23;11(1):182. doi: 10.1038/s41531-025-01050-8.
- Individual participant dataSharing planned: No. Individual participant data will not be shared because the speech and language recordings and related clinical information are sensitive and potentially identifiable. The study team will protect participant confidentiality and use the data only for the purposes described in the protocol.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Gaia C Santi, PhD contact
Petronilla Battista, PhD principal investigator
Istituti Clinici Scientifici Maugeri IRCCS Bari
Gaia Santi, PhD study chair
Istituti Clinici Scientifici Maugeri IRCCS Lumezzane
Simona Aresta, PhD study director
Istituti Clinici Scientifici Maugeri IRCCS Bari
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Speech and language Test to support clinical diagnosis in people with Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD), run by Istituti Clinici Scientifici Maugeri SpA at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 3 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Petronilla Battista, PhD (+390807814331, petronilla.battista@icsmaugeri.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance of AI-based language analysis in distinguishing diagnostic groups (time frame: Day 1 (At completion of the one-time remote speech and language assessment and analysis.)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07906769, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Nov 2026.