Alzheimer DiseaseDIADEMANCT06877182
Novel Neuroradiological Workflow for the Assisted DIAgnosis and Management of DEMentia with Artificial Intelligence
An observational study of Alzheimer Disease and Dementia, run by IRCCS SYNLAB SDN at 1 site in Italy.
Trial summary
Identifying, screening and monitoring individuals at risk of Alzheimer's disease (AD) and dementia is a formidable challenge. Neuroimaging, and in particular magnetic resonance imaging (MRI), is crucial to detect structural neurodegeneration. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Alzheimer Disease, Dementia
- What is being tested?
- Magnetic Resonance Imaging with Contrast
- How many people
- 80,000 participants
- Where
- 1 siteItaly
- Study dates
- 30 Aug 2024to 31 Aug 2027
- Phase
- Observational
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients who perform brain magnetic resonance during the last 20 years
Who may be able to join
- patients who perform brain magnetic resonance during the last 20 years
Who cannot join
- -
Trial locations and contacts
1 site in 1 country.
About this study
Identifying, screening and monitoring individuals at risk of Alzheimer's disease (AD) and dementia is a formidable challenge. Neuroimaging, and in particular magnetic resonance imaging (MRI), is crucial to detect structural neurodegeneration. However, current quantification tools are mainly limited to research contexts and produce non-standardised results. DIADEMA will build a systematic and standardised workflow to support neuro(radio)logical diagnosis. By combining artificial intelligence (AI) and machine learning (ML) the investigators will significantly enhance the clinical diagnosis of AD in neuroradiology. The investigator's main hypothesis is that an efficient workflow and associated higher diagnostic accuracy will substantially reduce healthcare costs, support clinical decision-making, provide second-opinion tools and improve patient care. This dual advance will have a profound impact on the healthcare system, marking an important step in the fight against Alzheimer's disease and dementia.
- Conditions
- Alzheimer Disease, Dementia
- Being tested
- Magnetic Resonance Imaging with Contrast
- Keywords
- Neuroimaging, Artificial Intelligence (AI), Dementia, Assisted Diagnosis, Magnetic Resonance Imaging (MRI)
- Medical subject headings
- Alzheimer Disease, Dementia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Magnetic Resonance Imaging with ContrastDiagnostic test
To evaluate the possibility to improve the neuroradiologic workflow with AI models
What is being measured
The primary outcome is the main question the study is designed to answer.
Improvement of the neuroradiological workflow performance
ROC curves
Measured: 1-36 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Mar 2025
- Study start30 Aug 2024
- Primary completion30 Dec 2025 (estimated)
- Study completion31 Aug 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (26)Papers the sponsor has linked to this study.
- Other trials for Alzheimer DiseaseStudies of the same condition, open and under way.
- 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
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 80,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- IRCCS SYNLAB SDN
- Sponsor type
- University, hospital or other
- Collaborators
- UNINA, UNITO, Ospedale Fate bene Fratelli di Brescia
- Responsible party
- Sponsor
- Investigators
- Marco Aiello, Principal investigator, IRCCS SYNLAB SDN
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06877182
- Sponsor's protocol ID
- 1/24
- Acronym
- DIADEMA
- Status last verified
- Mar 2025
- First submitted
- 10 Mar 2025
- Last updated
- 19 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Filip AC, Azevedo T, Passamonti L, Toschi N, Lio P. A novel Graph Attention Network Architecture for modeling multimodal brain connectivity. Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1071-1074. doi: 10.1109/EMBC44109.2020.9176613.
PubMed 33018171Spasov SE, Passamonti L, Duggento A, Lio P, Toschi N. A Multi-modal Convolutional Neural Network Framework for the Prediction of Alzheimer's Disease. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1271-1274. doi: 10.1109/EMBC.2018.8512468.
PubMed 30440622Spasov S, Passamonti L, Duggento A, Lio P, Toschi N; Alzheimer's Disease Neuroimaging Initiative. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. Neuroimage. 2019 Apr 1;189:276-287. doi: 10.1016/j.neuroimage.2019.01.031. Epub 2019 Jan 14.
PubMed 30654174Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS One. 2017 Sep 25;12(9):e0184661. doi: 10.1371/journal.pone.0184661. eCollection 2017.
PubMed 28945803van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol. 2021 Jun;31(6):3797-3804. doi: 10.1007/s00330-021-07892-z. Epub 2021 Apr 15.
PubMed 33856519Archetti D, Young AL, Oxtoby NP, Ferreira D, Martensson G, Westman E, Alexander DC, Frisoni GB, Redolfi A; for Alzheimer's Disease Neuroimaging Initiative and EuroPOND Consortium. Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease. Front Big Data. 2021 May 20;4:661110. doi: 10.3389/fdata.2021.661110. eCollection 2021.
PubMed 34095821Redolfi A, De Francesco S, Palesi F, Galluzzi S, Muscio C, Castellazzi G, Tiraboschi P, Savini G, Nigri A, Bottini G, Bruzzone MG, Ramusino MC, Ferraro S, Gandini Wheeler-Kingshott CAM, Tagliavini F, Frisoni GB, Ryvlin P, Demonet JF, Kherif F, Cappa SF, D'Angelo E. Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts. Front Neurol. 2020 Sep 23;11:1021. doi: 10.3389/fneur.2020.01021. eCollection 2020.
PubMed 33071930Ribaldi F, Altomare D, Jovicich J, Ferrari C, Picco A, Pizzini FB, Soricelli A, Mega A, Ferretti A, Drevelegas A, Bosch B, Muller BW, Marra C, Cavaliere C, Bartres-Faz D, Nobili F, Alessandrini F, Barkhof F, Gros-Dagnac H, Ranjeva JP, Wiltfang J, Kuijer J, Sein J, Hoffmann KT, Roccatagliata L, Parnetti L, Tsolaki M, Constantinidis M, Aiello M, Salvatore M, Montalti M, Caulo M, Didic M, Bargallo N, Blin O, Rossini PM, Schonknecht P, Floridi P, Payoux P, Visser PJ, Bordet R, Lopes R, Tarducci R, Bombois S, Hensch T, Fiedler U, Richardson JC, Frisoni GB, Marizzoni M. Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study. Magn Reson Imaging. 2021 Feb;76:108-115. doi: 10.1016/j.mri.2020.11.008. Epub 2020 Nov 19.
PubMed 33220450Morid MA, Borjali A, Del Fiol G. A scoping review of transfer learning research on medical image analysis using ImageNet. Comput Biol Med. 2021 Jan;128:104115. doi: 10.1016/j.compbiomed.2020.104115. Epub 2020 Nov 13.
PubMed 33227578Bao S, Boyd BD, Kanakaraj P, Ramadass K, Meyer FAC, Liu Y, Duett WE, Huo Y, Lyu I, Zald DH, Smith SA, Rogers BP, Landman BA. Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis. J Digit Imaging. 2022 Dec;35(6):1576-1589. doi: 10.1007/s10278-022-00679-8. Epub 2022 Aug 3.
PubMed 35922700Rajput D, Wang WJ, Chen CC. Evaluation of a decided sample size in machine learning applications. BMC Bioinformatics. 2023 Feb 14;24(1):48. doi: 10.1186/s12859-023-05156-9.
PubMed 36788550McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Thies B, Weintraub S, Phelps CH. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011 May;7(3):263-9. doi: 10.1016/j.jalz.2011.03.005. Epub 2011 Apr 21.
PubMed 21514250Boccardi M, Altomare D, Ferrari C, Festari C, Guerra UP, Paghera B, Pizzocaro C, Lussignoli G, Geroldi C, Zanetti O, Cotelli MS, Turla M, Borroni B, Rozzini L, Mirabile D, Defanti C, Gennuso M, Prelle A, Gentile S, Morandi A, Vollaro S, Volta GD, Bianchetti A, Conti MZ, Cappuccio M, Carbone P, Bellandi D, Abruzzi L, Bettoni L, Villani D, Raimondi MC, Lanari A, Ciccone A, Facchi E, Di Fazio I, Rozzini R, Boffelli S, Manzoni L, Salvi GP, Cavaliere S, Belotti G, Avanzi S, Pasqualetti P, Muscio C, Padovani A, Frisoni GB; Incremental Diagnostic Value of Amyloid PET With [18F]-Florbetapir (INDIA-FBP) Working Group. Assessment of the Incremental Diagnostic Value of Florbetapir F 18 Imaging in Patients With Cognitive Impairment: The Incremental Diagnostic Value of Amyloid PET With [18F]-Florbetapir (INDIA-FBP) Study. JAMA Neurol. 2016 Dec 1;73(12):1417-1424. doi: 10.1001/jamaneurol.2016.3751.
PubMed 27802513Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB, Hampel H, Jagust WJ, Johnson KA, Knopman DS, Petersen RC, Scheltens P, Sperling RA, Dubois B. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016 Aug 2;87(5):539-47. doi: 10.1212/WNL.0000000000002923. Epub 2016 Jul 1.
PubMed 27371494Teipel S, Grothe MJ; Alzheimer's Disease Neuroimaging Initiative. MRI-based basal forebrain atrophy and volumetric signatures associated with limbic TDP-43 compared to Alzheimer's disease pathology. Neurobiol Dis. 2023 May;180:106070. doi: 10.1016/j.nbd.2023.106070. Epub 2023 Mar 8.
PubMed 36898615
Study at a glance
From the registry record, last updated by the sponsor on 19 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Novel Neuroradiological Workflow for the Assisted DIAgnosis and Management of DEMentia with Artificial Intelligence
- ClinicalTrials.gov ID
- NCT06877182
- Acronym / sponsor ID
- DIADEMA / 1/24
- Conditions
- Alzheimer Disease, Dementia
- Keywords
- Neuroimaging, Artificial Intelligence (AI), Dementia, Assisted Diagnosis, Magnetic Resonance Imaging (MRI)
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 80,000 participants (estimated)
- Lead sponsor
- IRCCS SYNLAB SDNUniversity, hospital or other
- Collaborators
- UNINA, UNITO, Ospedale Fate bene Fratelli di Brescia
- Study start
- 30 Aug 2024 (actual)
- Primary completion
- 30 Dec 2025 (estimated)
- Study completion
- 31 Aug 2027 (estimated)
- First posted
- 14 Mar 2025 (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.
Identifying, screening and monitoring individuals at risk of Alzheimer's disease (AD) and dementia is a formidable challenge. Neuroimaging, and in particular magnetic resonance imaging (MRI), is crucial to detect structural neurodegeneration. However, current quantification tools are mainly limited to research contexts and produce non-standardised results. DIADEMA will build a systematic and standardised workflow to support neuro(radio)logical diagnosis. By combining artificial intelligence (AI) and machine learning (ML) the investigators will significantly enhance the clinical diagnosis of AD in neuroradiology. The investigator's main hypothesis is that an efficient workflow and associated higher diagnostic accuracy will substantially reduce healthcare costs, support clinical decision-making, provide second-opinion tools and improve patient care. This dual advance will have a profound impact on the healthcare system, marking an important step in the fight against Alzheimer's disease and dementia.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients who perform brain magnetic resonance during the last 20 years
Key inclusion criteria (1 listed)
- patients who perform brain magnetic resonance during the last 20 years
Key exclusion criteria (1 listed)
- -
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 |
|---|---|---|
| Magnetic Resonance Imaging with ContrastTo evaluate the possibility to improve the neuroradiologic workflow with AI models | Diagnostic test | – |
Primary outcome
Improvement of the neuroradiological workflow performance
Time frame: 1-36 month
ROC curves
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 19 Mar 2025.
- Background: PubMed 33018171Filip AC, Azevedo T, Passamonti L, Toschi N, Lio P. A novel Graph Attention Network Architecture for modeling multimodal brain connectivity. Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1071-1074. doi: 10.1109/EMBC44109.2020.9176613.
- Background: PubMed 30440622Spasov SE, Passamonti L, Duggento A, Lio P, Toschi N. A Multi-modal Convolutional Neural Network Framework for the Prediction of Alzheimer's Disease. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1271-1274. doi: 10.1109/EMBC.2018.8512468.
- Background: PubMed 30654174Spasov S, Passamonti L, Duggento A, Lio P, Toschi N; Alzheimer's Disease Neuroimaging Initiative. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. Neuroimage. 2019 Apr 1;189:276-287. doi: 10.1016/j.neuroimage.2019.01.031. Epub 2019 Jan 14.
- Background: PubMed 28945803Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS One. 2017 Sep 25;12(9):e0184661. doi: 10.1371/journal.pone.0184661. eCollection 2017.
- Background: PubMed 33856519van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol. 2021 Jun;31(6):3797-3804. doi: 10.1007/s00330-021-07892-z. Epub 2021 Apr 15.
- Background: PubMed 34095821Archetti D, Young AL, Oxtoby NP, Ferreira D, Martensson G, Westman E, Alexander DC, Frisoni GB, Redolfi A; for Alzheimer's Disease Neuroimaging Initiative and EuroPOND Consortium. Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease. Front Big Data. 2021 May 20;4:661110. doi: 10.3389/fdata.2021.661110. eCollection 2021.
- Background: PubMed 33071930Redolfi A, De Francesco S, Palesi F, Galluzzi S, Muscio C, Castellazzi G, Tiraboschi P, Savini G, Nigri A, Bottini G, Bruzzone MG, Ramusino MC, Ferraro S, Gandini Wheeler-Kingshott CAM, Tagliavini F, Frisoni GB, Ryvlin P, Demonet JF, Kherif F, Cappa SF, D'Angelo E. Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts. Front Neurol. 2020 Sep 23;11:1021. doi: 10.3389/fneur.2020.01021. eCollection 2020.
- Background: PubMed 33220450Ribaldi F, Altomare D, Jovicich J, Ferrari C, Picco A, Pizzini FB, Soricelli A, Mega A, Ferretti A, Drevelegas A, Bosch B, Muller BW, Marra C, Cavaliere C, Bartres-Faz D, Nobili F, Alessandrini F, Barkhof F, Gros-Dagnac H, Ranjeva JP, Wiltfang J, Kuijer J, Sein J, Hoffmann KT, Roccatagliata L, Parnetti L, Tsolaki M, Constantinidis M, Aiello M, Salvatore M, Montalti M, Caulo M, Didic M, Bargallo N, Blin O, Rossini PM, Schonknecht P, Floridi P, Payoux P, Visser PJ, Bordet R, Lopes R, Tarducci R, Bombois S, Hensch T, Fiedler U, Richardson JC, Frisoni GB, Marizzoni M. Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study. Magn Reson Imaging. 2021 Feb;76:108-115. doi: 10.1016/j.mri.2020.11.008. Epub 2020 Nov 19.
- Background: PubMed 33227578Morid MA, Borjali A, Del Fiol G. A scoping review of transfer learning research on medical image analysis using ImageNet. Comput Biol Med. 2021 Jan;128:104115. doi: 10.1016/j.compbiomed.2020.104115. Epub 2020 Nov 13.
- Background: PubMed 35922700Bao S, Boyd BD, Kanakaraj P, Ramadass K, Meyer FAC, Liu Y, Duett WE, Huo Y, Lyu I, Zald DH, Smith SA, Rogers BP, Landman BA. Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis. J Digit Imaging. 2022 Dec;35(6):1576-1589. doi: 10.1007/s10278-022-00679-8. Epub 2022 Aug 3.
- Background: PubMed 36788550Rajput D, Wang WJ, Chen CC. Evaluation of a decided sample size in machine learning applications. BMC Bioinformatics. 2023 Feb 14;24(1):48. doi: 10.1186/s12859-023-05156-9.
- Background: PubMed 21514250McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Thies B, Weintraub S, Phelps CH. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011 May;7(3):263-9. doi: 10.1016/j.jalz.2011.03.005. Epub 2011 Apr 21.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
Marco Aiello principal investigator
IRCCS SYNLAB SDN
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Alzheimer Disease and Dementia, run by IRCCS SYNLAB SDN 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 1 inclusion and 1 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: under way, not looking for participants, as of the sponsor's last update on 19 Mar 2025. 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Improvement of the neuroradiological workflow performance (time frame: 1-36 month). 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 NCT06877182, which also lists 26 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 19 Mar 2025.