Multimodal Deep Learning for the Diagnosis and Assessment of Alzheimer's Disease

NCT06081569 | Not Yet Recruiting

• 1 other identifier: LHuayan
• Study Type: observational
• Target: 300
• Locations: 0 countries
• Sites: N/A

Brief Summary

Alzheimer's disease (AD) is the most common dementia and has been one of the most expensive diseases with the highest lethality. With the rapid increase of the aging population, more and more burdens will be posed on society and economics. The manifestations of AD are the progressive loss of memory, language and visuospatial function, executive and daily living abilities, and so forth. The Pathophysiological changes of AD occur 10-20 years before the clinical symptoms, while there is still a lack of effective strategy for early diagnosis. Mild cognitive impairment (MCI) is considered to be a transitional state between healthy aging and the clinical diagnosis of dementia and has received increasing attention as a separate diagnostic entity. To make the diagnosis, doctors ought to compressively consider the multimodal medical information including clinical symptoms, neuroimages, neuropsychological tests, laboratory examinations, etc. Multimodal deep learning has risen to this challenge， which could integrate the various modalities of biological information and capture the relationships among them contributing to higher accuracy and efficiency. It has been widely applied in imaging, tumor pathology, genomics, etc. Recently, the studies on AD based on deep learning still mainly focused on multimodal neuroimaging, while multimodal medical information requires comprehensive integration and intellectual analysis. Moreover, studies reveal that some imperceptible symptoms in MCI and the early stage of AD may also play an effective role in diagnosis and assessment, such as gait disorder, facial expression identification dysfunction, and speech and language impairment. However, doctors could hardly detect the slight and complex changes, which could rely on the full mining of the video and audio information by multimodal deep learning. In conclusion, we aim to explore the features of gait disorder, facial expression identification dysfunction, and speech and language impairment in MCI and AD, and analyze their diagnostic efficiency. We would identify the different degrees of dependency on multimodal medical information in diagnosis and finally build an optimal multimodal diagnostic method utilizing the most convenient and economical information. Besides, based on follow-up observations on the changes in multimodal medical information with the progress of AD and MCI, we expect to establish an effective and convenient diagnostic strategy.

Conditions

Alzheimer Disease; Mild Cognitive Impairment

Keywords

multimodal deep learning; diagnosis; Alzheimer disease; Mild Cognitive Impairment; assessment

Outcome Measures

Primary Outcomes (1)

• The diagnostic efficiency of multimodal deep learning diagnostic strategy

  The diagnostic efficiency will be measured by the area under curve（AUC）of receiver operating characteristic（ROC）curve.

  The outcome will be measured and analyzed once all the baseline multimodal medical information has been collected.

Secondary Outcomes (1)

• The prognostic efficiency of multimodal deep learning prognostic strategy

  The outcome will be measured and analyzed once all two-year follow-up multimodal medical information has been collected.

Study Arms (3)

Alzheimer's disease

the diagnosis of AD is according to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for AD.

Diagnostic Test: gait video; speech video; facial expression video;

Mild cognitive impairment

the diagnosis of MCI refers to the criteria defined by Peterson in 2004.

Diagnostic Test: gait video; speech video; facial expression video;

Control

participants who are age-matched with AD and MCI participants, without cognitive impairment.

Diagnostic Test: gait video; speech video; facial expression video;

Interventions

gait video; speech video; facial expression video; DIAGNOSTIC_TEST

The videos of participants' gait, facial expression, and speech will be recorded and analyzed further. Other routine diagnostic tests will also be performed such as imaging of MRI, cognitive scales, etc.

Also known as: Other routine diagnostic tests such as imaging, cognitive scales, etc.

Alzheimer's disease; Control; Mild cognitive impairment

Eligibility Criteria

• Age: 50 Years - 85 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

We plan to include 100 AD, 100 MCI, and 100 control participants. These participants are those who visit our hospital from 1st October 2023 to 1st March 2024.

You may qualify if:

• Participants' age is between 50 and 85 years old, male or female;
• Participants graduated from primary school or above, with normal hearing, vision, and pronunciation, using Chinese as their mother tongue and Mandarin as their daily language；
• The diagnosis of AD and MCI participants conform to the corresponding diagnostic criteria mentioned above;
• The scores of MMSE are between 10 and 28, and the scores of CDR are no more than 2.
• Patients or family members agree to sign informed consent.

You may not qualify if:

• Participants suffer from neurological disorders that could cause dysfunction of the brain, such as depression, tumors, Parkinson's disease, metabolic encephalopathy, encephalitis, multiple sclerosis, epilepsy, brain trauma, normal cranial pressure hydrocephalus, and so forth;
• Participants suffer from systematic diseases that could cause cognitive impairment, such as liver insufficiency, renal insufficiency, thyroid dysfunction, severe anemia, folic acid or vitamin B12 deficiency, syphilis, HIV infection, alcohol and drug abuse, and so forth;
• Participants suffer from diseases that are unable to cooperate with the examinations;
• Participants cannot take magnetic resonance imaging;
• Participants suffer from mental and neurodevelopmental retardation;
• Participants refuse to sign informed consent.

Sponsors & Collaborators

• First Hospital of China Medical University: lead

Related Publications (19)

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Biospecimen

Retention: SAMPLES WITH DNA

Samples of whole blood are retained and saved at -80 centigrade.

MeSH Terms

Conditions

Alzheimer Disease; Cognitive Dysfunction; Disease

Interventions

Diagnostic Imaging

Condition Hierarchy (Ancestors)

Dementia; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Tauopathies; Neurodegenerative Diseases; Neurocognitive Disorders; Mental Disorders; Cognition Disorders; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Diagnostic Techniques and Procedures; Diagnosis

Study Officials

• Huayan Liu

  the first affiliated hospital of China medical university, neurology department

  STUDY CHAIR

Central Study Contacts

Huayan Liu, PhD.

CONTACT

+86 13609831417; liuhy@cmu1h.com

Boru Jin, PhD.

CONTACT

+86 13614031943; jin_boru@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Neurology department

Study Record Dates

• First Submitted: October 7, 2023
• First Posted: October 13, 2023
• Study Start: October 15, 2023
• Primary Completion: October 15, 2024
• Study Completion (Estimated): October 15, 2026
• Last Updated: October 13, 2023

Record last verified: 2023-10

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR
• Time Frame: starting 12 months after publication
• Access Criteria: the IPD and any additional supporting information will be shared with the researchers who follow our idea and theory, and concern on the AD and MCI diagnosis. Huayan Liu will review the requests.