Alzheimer DiseaseNCT06081569
Multimodal Deep Learning for the Diagnosis and Assessment of Alzheimer's Disease
An observational study of Alzheimer Disease and Mild Cognitive Impairment, run by First Hospital of China Medical University.
Trial 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. Read more about this study
- Who is it for?
- Ages 50 to 85 yearsHealthy volunteers can take part
- Conditions the study is for
- Alzheimer Disease, Mild Cognitive Impairment
- What is being tested?
- gait video; speech video; facial expression video;
- How many people
- 300 participants
- Where
- No sites listed yet
- Study dates
- 15 Oct 2023to 15 Oct 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
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 50 to 85 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- . 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.
Who cannot join
- . 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.
Trial locations and contacts
No sites are listed yet.
About this study
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.
Read the detailed description
Our objective is to make the early diagnosis and assessment of AD and MCI based on multimodal deep learning. Initially, Gait disorder, facial expression identification dysfunction, and speech and language impairment are of great significance in the occurrence and development of AD and MCI. However, due to the high complexity and stealthiness of these clinical symptoms, no uniform conclusions have been made. Hence, we attempt to apply machine learning methods to recognize the video and audio information. In this way, we will explore the changing characteristics of gait, expression, and language in AD and MCI, and analyze their diagnostic effectiveness as diagnostic markers, finally providing new ideas and experimental data for the diagnosis of AD and MCI. Secondly, multimodal medical information needs to be integrated and comprehensively analyzed. We aim to propose an optimal diagnostic strategy referring to the different degrees of dependency on multimodal medical information in diagnosis. Moreover, observing the changes in multimodal medical information with the progress of AD and MCI, we expect to build a predicting model of AD diagnosis and prognosis.
The methods are as follows:
- Collecting multimodal medical information A variety of multimodal medical information would be carefully collected including the baseline demographic data, chief complaint and medical history, peripheral organ function assessment, laboratory examination, imaging examination, neuroelectrophysiological examination, neurocognitive and psychological examination, information on gait, expression, and language, and biological samples, etc.
- Revealing the changes of gait, expression, and language in patients with AD and MCI, and verifying their diagnostic efficacy. For multimodal medical information on gait, OpenPose model was used to extract human key points and construct a human skeleton structure diagram. Based on graph neural networks and convolutional neural networks, instantaneous action analysis of single-frame images is carried out. And then utilizing the Transformer model, gait sequence analysis is carried out by integrating multi-frame video.For multimodal medical information on facial expression, the Dlib algorithm will be used to extract facial key points, combined with facial expression images, and the spatiotemporal Transformer model will be used for facial expression analysis. For multimodal medical information on language, ASRT model will be used for speech recognition and text content extraction. Simultaneously, the frequency domain Fourier transform and wavelet transform will be applied to extract frequency domain information and analyze the speech features by integrating language content, voice intonation, speech speed, and other information. Based on the attention model, the gait, expression, and language analysis results of AD and MCI will be compared with those of the control group to reveal the features of AD and MCI and provide evidence for disease diagnosis.
- Analyzing the different degrees of dependency on multimodal information in the diagnosis of AD and MCI diseases, and establishing an optimal diagnosis strategy In the supervised learning process, the attention mechanism-based method will be used to analyze the influence of multimodal information on the final results. At the same time, based on the knowledge map, the patient's blood biochemical indicators, genomic information and other fields of knowledge would be added to the model. Based on Bayesian probability inference and causal inference theory, the causal programming method will be used to model the causal analysis of information and diagnosis results of different modes. Based on AutoML method, multimodal information will be combined and optimized, and a reliable optimal diagnosis strategy will be established according to experimental results.
- Exploring the changes of multimodal medical information with the progression of the disease, and build a predicting model for early diagnosis and disease progression of AD.
Viewing multimodal medical information as the control condition, the Transformer model will be used to model time sequence information, and the conditional diffusion model will be used to generate patients' MRI image changes and other disease progression-related information, providing the basis for disease progression prediction. Based on the large multimodal model technology, the output of the model will be interfered with and adjusted referring to the judgment and description of professional doctors, to generate the prediction in line with the judgment of professional doctors, and finally construct the interpretable early diagnosis and disease progression prediction model.
- Conditions
- Alzheimer Disease, Mild Cognitive Impairment
- Being tested
- gait video; speech video; facial expression video;
- Keywords
- multimodal deep learning, diagnosis, Alzheimer disease, Mild Cognitive Impairment, assessment
- Medical subject headings
- Alzheimer Disease, Cognitive Dysfunction, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
gait video; speech video; facial expression video;Diagnostic test
Also called Other routine diagnostic tests such as imaging, cognitive scales, etc.
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Diagnostic Test: gait video; speech video; facial expression video; | |
| Control | participants who are age-matched with AD and MCI participants, without cognitive impairment. Receives: Diagnostic Test: gait video; speech video; facial expression video; |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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 prognostic efficiency will be measured by the area under curve（AUC）of receiver operating characteristic（ROC）curve.
Measured: The outcome will be measured and analyzed once all two-year follow-up multimodal medical information has been collected.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Oct 2023
- Study start15 Oct 2023 (estimated)
- Primary completion15 Oct 2024 (estimated)
- Study completion15 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (19)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
- Prospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- First Hospital of China Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Liu Huayan, First Hospital of China Medical University
- Investigators
- Huayan Liu, Study chair, the first affiliated hospital of China medical university, neurology department
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06081569
- Sponsor's protocol ID
- LHuayan
- Status last verified
- Oct 2023
- First submitted
- 7 Oct 2023
- Last updated
- 13 Oct 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: There is a plan to make IPD and related data dictionaries available.
Publications
Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chetelat G, Teunissen CE, Cummings J, van der Flier WM. Alzheimer's disease. Lancet. 2021 Apr 24;397(10284):1577-1590. doi: 10.1016/S0140-6736(20)32205-4. Epub 2021 Mar 2.
PubMed 336674162023 Alzheimer's disease facts and figures. Alzheimers Dement. 2023 Apr;19(4):1598-1695. doi: 10.1002/alz.13016. Epub 2023 Mar 14.
PubMed 36918389Knopman DS, Amieva H, Petersen RC, Chetelat G, Holtzman DM, Hyman BT, Nixon RA, Jones DT. Alzheimer disease. Nat Rev Dis Primers. 2021 May 13;7(1):33. doi: 10.1038/s41572-021-00269-y.
PubMed 33986301Jia J, Wei C, Chen S, Li F, Tang Y, Qin W, Zhao L, Jin H, Xu H, Wang F, Zhou A, Zuo X, Wu L, Han Y, Han Y, Huang L, Wang Q, Li D, Chu C, Shi L, Gong M, Du Y, Zhang J, Zhang J, Zhou C, Lv J, Lv Y, Xie H, Ji Y, Li F, Yu E, Luo B, Wang Y, Yang S, Qu Q, Guo Q, Liang F, Zhang J, Tan L, Shen L, Zhang K, Zhang J, Peng D, Tang M, Lv P, Fang B, Chu L, Jia L, Gauthier S. The cost of Alzheimer's disease in China and re-estimation of costs worldwide. Alzheimers Dement. 2018 Apr;14(4):483-491. doi: 10.1016/j.jalz.2017.12.006. Epub 2018 Feb 9.
PubMed 29433981Wilson J, Allcock L, Mc Ardle R, Taylor JP, Rochester L. The neural correlates of discrete gait characteristics in ageing: A structured review. Neurosci Biobehav Rev. 2019 May;100:344-369. doi: 10.1016/j.neubiorev.2018.12.017. Epub 2018 Dec 13.
PubMed 30552912Beauchet O, Launay CP, Annweiler C, Allali G. Hippocampal volume, early cognitive decline and gait variability: which association? Exp Gerontol. 2015 Jan;61:98-104. doi: 10.1016/j.exger.2014.11.002. Epub 2014 Nov 6.
PubMed 25446977Ceresetti R, Rouch I, Laurent B, Getenet JC, Pommier M, de Chalvron S, Chainay H, Borg C. Processing of Facial Expressions of Emotions and Pain in Alzheimer's Disease. J Alzheimers Dis. 2022;89(1):389-398. doi: 10.3233/JAD-220236.
PubMed 35871339Klein-Koerkamp Y, Beaudoin M, Baciu M, Hot P. Emotional decoding abilities in Alzheimer's disease: a meta-analysis. J Alzheimers Dis. 2012;32(1):109-25. doi: 10.3233/JAD-2012-120553.
PubMed 22776967Rosen HJ, Wilson MR, Schauer GF, Allison S, Gorno-Tempini ML, Pace-Savitsky C, Kramer JH, Levenson RW, Weiner M, Miller BL. Neuroanatomical correlates of impaired recognition of emotion in dementia. Neuropsychologia. 2006;44(3):365-73. doi: 10.1016/j.neuropsychologia.2005.06.012. Epub 2005 Sep 9.
PubMed 16154603Adolphs R, Tranel D. Impaired judgments of sadness but not happiness following bilateral amygdala damage. J Cogn Neurosci. 2004 Apr;16(3):453-62. doi: 10.1162/089892904322926782.
PubMed 15072680Yamaguchi T, Maki Y, Yamaguchi H. Yamaguchi Facial Expression-Making Task in Alzheimer's Disease: A Novel and Enjoyable Make-a-Face Game. Dement Geriatr Cogn Dis Extra. 2012 Jan;2(1):248-57. doi: 10.1159/000339425. Epub 2012 Jun 20.
PubMed 22811688Eyigoz E, Mathur S, Santamaria M, Cecchi G, Naylor M. Linguistic markers predict onset of Alzheimer's disease. EClinicalMedicine. 2020 Oct 22;28:100583. doi: 10.1016/j.eclinm.2020.100583. eCollection 2020 Nov.
PubMed 33294808Ahmed S, Haigh AM, de Jager CA, Garrard P. Connected speech as a marker of disease progression in autopsy-proven Alzheimer's disease. Brain. 2013 Dec;136(Pt 12):3727-37. doi: 10.1093/brain/awt269. Epub 2013 Oct 18.
PubMed 24142144Pakhomov SV, Hemmy LS. A computational linguistic measure of clustering behavior on semantic verbal fluency task predicts risk of future dementia in the nun study. Cortex. 2014 Jun;55:97-106. doi: 10.1016/j.cortex.2013.05.009. Epub 2013 Jun 14.
PubMed 23845236Green S, Reivonen S, Rutter LM, Nouzova E, Duncan N, Clarke C, MacLullich AMJ, Tieges Z. Investigating speech and language impairments in delirium: A preliminary case-control study. PLoS One. 2018 Nov 26;13(11):e0207527. doi: 10.1371/journal.pone.0207527. eCollection 2018.
PubMed 30475831
Study at a glance
From the registry record, last updated by the sponsor on 13 Oct 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal Deep Learning for the Diagnosis and Assessment of Alzheimer's Disease
- ClinicalTrials.gov ID
- NCT06081569
- Acronym / sponsor ID
- LHuayan
- Conditions
- Alzheimer Disease, Mild Cognitive Impairment
- Keywords
- multimodal deep learning, diagnosis, Alzheimer disease, Mild Cognitive Impairment, assessment
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- First Hospital of China Medical UniversityUniversity, hospital or other
- Study start
- 15 Oct 2023 (estimated)
- Primary completion
- 15 Oct 2024 (estimated)
- Study completion
- 15 Oct 2026 (estimated)
- First posted
- 13 Oct 2023 (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.
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.
Detailed description
Our objective is to make the early diagnosis and assessment of AD and MCI based on multimodal deep learning. Initially, Gait disorder, facial expression identification dysfunction, and speech and language impairment are of great significance in the occurrence and development of AD and MCI. However, due to the high complexity and stealthiness of these clinical symptoms, no uniform conclusions have been made. Hence, we attempt to apply machine learning methods to recognize the video and audio information. In this way, we will explore the changing characteristics of gait, expression, and language in AD and MCI, and analyze their diagnostic effectiveness as diagnostic markers, finally providing new ideas and experimental data for the diagnosis of AD and MCI. Secondly, multimodal medical information needs to be integrated and comprehensively analyzed. We aim to propose an optimal diagnostic strategy referring to the different degrees of dependency on multimodal medical information in diagnosis. Moreover, observing the changes in multimodal medical information with the progress of AD and MCI, we expect to build a predicting model of AD diagnosis and prognosis.
The methods are as follows:
- Collecting multimodal medical information A variety of multimodal medical information would be carefully collected including the baseline demographic data, chief complaint and medical history, peripheral organ function assessment, laboratory examination, imaging examination, neuroelectrophysiological examination, neurocognitive and psychological examination, information on gait, expression, and language, and biological samples, etc.
- Revealing the changes of gait, expression, and language in patients with AD and MCI, and verifying their diagnostic efficacy. For multimodal medical information on gait, OpenPose model was used to extract human key points and construct a human skeleton structure diagram. Based on graph neural networks and convolutional neural networks, instantaneous action analysis of single-frame images is carried out. And then utilizing the Transformer model, gait sequence analysis is carried out by integrating multi-frame video.For multimodal medical information on facial expression, the Dlib algorithm will be used to extract facial key points, combined with facial expression images, and the spatiotemporal Transformer model will be used for facial expression analysis. For multimodal medical information on language, ASRT model will be used for speech recognition and text content extraction. Simultaneously, the frequency domain Fourier transform and wavelet transform will be applied to extract frequency domain information and analyze the speech features by integrating language content, voice intonation, speech speed, and other information. Based on the attention model, the gait, expression, and language analysis results of AD and MCI will be compared with those of the control group to reveal the features of AD and MCI and provide evidence for disease diagnosis.
- Analyzing the different degrees of dependency on multimodal information in the diagnosis of AD and MCI diseases, and establishing an optimal diagnosis strategy In the supervised learning process, the attention mechanism-based method will be used to analyze the influence of multimodal information on the final results. At the same time, based on the knowledge map, the patient's blood biochemical indicators, genomic information and other fields of knowledge would be added to the model. Based on Bayesian probability inference and causal inference theory, the causal programming method will be used to model the causal analysis of information and diagnosis results of different modes. Based on AutoML method, multimodal information will be combined and optimized, and a reliable optimal diagnosis strategy will be established according to experimental results.
- Exploring the changes of multimodal medical information with the progression of the disease, and build a predicting model for early diagnosis and disease progression of AD.
Viewing multimodal medical information as the control condition, the Transformer model will be used to model time sequence information, and the conditional diffusion model will be used to generate patients' MRI image changes and other disease progression-related information, providing the basis for disease progression prediction. Based on the large multimodal model technology, the output of the model will be interfered with and adjusted referring to the judgment and description of professional doctors, to generate the prediction in line with the judgment of professional doctors, and finally construct the interpretable early diagnosis and disease progression prediction model.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Alzheimer's disease | – | Diagnostic Test: gait video; speech video; facial expression video;the diagnosis of AD is according to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for AD. |
| Mild cognitive impairment | – | Diagnostic Test: gait video; speech video; facial expression video;the diagnosis of MCI refers to the criteria defined by Peterson in 2004. |
| Control | – | Diagnostic Test: gait video; speech video; facial expression video;participants who are age-matched with AD and MCI participants, without cognitive impairment. |
Who may qualify
- Ages
- 50 to 85 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (5 listed)
- . 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.
Key exclusion criteria (6 listed)
- . 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;
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 |
|---|---|---|
| gait video; speech video; facial expression video;Also: Other routine diagnostic tests such as imaging, cognitive scales, etc.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. | Diagnostic test | Alzheimer's disease, Control, Mild cognitive impairment |
Primary outcome
The diagnostic efficiency of multimodal deep learning diagnostic strategy
Time frame: The outcome will be measured and analyzed once all the baseline multimodal medical information has been collected.
The diagnostic efficiency will be measured by the area under curve（AUC）of receiver operating characteristic（ROC）curve.
1 secondary outcome
The prognostic efficiency of multimodal deep learning prognostic strategy
Time frame: The outcome will be measured and analyzed once all two-year follow-up multimodal medical information has been collected.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Oct 2023.
- Background: PubMed 33667416Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chetelat G, Teunissen CE, Cummings J, van der Flier WM. Alzheimer's disease. Lancet. 2021 Apr 24;397(10284):1577-1590. doi: 10.1016/S0140-6736(20)32205-4. Epub 2021 Mar 2.
- Background: PubMed 369183892023 Alzheimer's disease facts and figures. Alzheimers Dement. 2023 Apr;19(4):1598-1695. doi: 10.1002/alz.13016. Epub 2023 Mar 14.
- Background: PubMed 33986301Knopman DS, Amieva H, Petersen RC, Chetelat G, Holtzman DM, Hyman BT, Nixon RA, Jones DT. Alzheimer disease. Nat Rev Dis Primers. 2021 May 13;7(1):33. doi: 10.1038/s41572-021-00269-y.
- Background: PubMed 29433981Jia J, Wei C, Chen S, Li F, Tang Y, Qin W, Zhao L, Jin H, Xu H, Wang F, Zhou A, Zuo X, Wu L, Han Y, Han Y, Huang L, Wang Q, Li D, Chu C, Shi L, Gong M, Du Y, Zhang J, Zhang J, Zhou C, Lv J, Lv Y, Xie H, Ji Y, Li F, Yu E, Luo B, Wang Y, Yang S, Qu Q, Guo Q, Liang F, Zhang J, Tan L, Shen L, Zhang K, Zhang J, Peng D, Tang M, Lv P, Fang B, Chu L, Jia L, Gauthier S. The cost of Alzheimer's disease in China and re-estimation of costs worldwide. Alzheimers Dement. 2018 Apr;14(4):483-491. doi: 10.1016/j.jalz.2017.12.006. Epub 2018 Feb 9.
- Background: PubMed 30552912Wilson J, Allcock L, Mc Ardle R, Taylor JP, Rochester L. The neural correlates of discrete gait characteristics in ageing: A structured review. Neurosci Biobehav Rev. 2019 May;100:344-369. doi: 10.1016/j.neubiorev.2018.12.017. Epub 2018 Dec 13.
- Background: PubMed 25446977Beauchet O, Launay CP, Annweiler C, Allali G. Hippocampal volume, early cognitive decline and gait variability: which association? Exp Gerontol. 2015 Jan;61:98-104. doi: 10.1016/j.exger.2014.11.002. Epub 2014 Nov 6.
- Background: PubMed 35871339Ceresetti R, Rouch I, Laurent B, Getenet JC, Pommier M, de Chalvron S, Chainay H, Borg C. Processing of Facial Expressions of Emotions and Pain in Alzheimer's Disease. J Alzheimers Dis. 2022;89(1):389-398. doi: 10.3233/JAD-220236.
- Background: PubMed 22776967Klein-Koerkamp Y, Beaudoin M, Baciu M, Hot P. Emotional decoding abilities in Alzheimer's disease: a meta-analysis. J Alzheimers Dis. 2012;32(1):109-25. doi: 10.3233/JAD-2012-120553.
- Background: PubMed 16154603Rosen HJ, Wilson MR, Schauer GF, Allison S, Gorno-Tempini ML, Pace-Savitsky C, Kramer JH, Levenson RW, Weiner M, Miller BL. Neuroanatomical correlates of impaired recognition of emotion in dementia. Neuropsychologia. 2006;44(3):365-73. doi: 10.1016/j.neuropsychologia.2005.06.012. Epub 2005 Sep 9.
- Background: PubMed 15072680Adolphs R, Tranel D. Impaired judgments of sadness but not happiness following bilateral amygdala damage. J Cogn Neurosci. 2004 Apr;16(3):453-62. doi: 10.1162/089892904322926782.
- Background: PubMed 22811688Yamaguchi T, Maki Y, Yamaguchi H. Yamaguchi Facial Expression-Making Task in Alzheimer's Disease: A Novel and Enjoyable Make-a-Face Game. Dement Geriatr Cogn Dis Extra. 2012 Jan;2(1):248-57. doi: 10.1159/000339425. Epub 2012 Jun 20.
- Background: PubMed 33294808Eyigoz E, Mathur S, Santamaria M, Cecchi G, Naylor M. Linguistic markers predict onset of Alzheimer's disease. EClinicalMedicine. 2020 Oct 22;28:100583. doi: 10.1016/j.eclinm.2020.100583. eCollection 2020 Nov.
- Individual participant dataSharing planned: Yes. There is a plan to make IPD and related data dictionaries available.
Related trials for Alzheimer Disease
RecruitingPhase 2
Neuroplasticity Enhancement From Cognitive Training Reinforced by Psilocybin
Alzheimer's Disease
Not yet recruiting
Acupressure as Long-term Intervention to Slow the Progression of Early/Mild Alzheimer's Disease
Alzheimer's Disease
RecruitingObservational study
Effectiveness and Safety of Disease-Modifying Therapy for Alzheimer's Disease: A Multicenter Retrospective Study
Alzheimer's Disease (AD)
RecruitingPhase 3
A Phase 3 Study to Evaluate the Safety and Efficacy of KarXT + KarX-EC for the Treatment of Agitation Associated With Alzheimer's Disease (ADAGIO-1)
Alzheimer Disease
RecruitingPhase 3
A Study of Trontinemab in Cognitively Unimpaired Individuals at Risk for Progression to Symptomatic Alzheimer's Disease
Alzheimer's Disease
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.
Huayan Liu study chair
the first affiliated hospital of China medical university, neurology department
Questions about this trial
What is this clinical trial studying?
An observational study of Alzheimer Disease and Mild Cognitive Impairment, run by First Hospital of China Medical University.
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 5 inclusion and 6 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: status not confirmed, as of the sponsor's last update on 13 Oct 2023. Status can change between updates, so confirm with the study contact before making plans.
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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Huayan Liu, PhD. (+86 13609831417, liuhy@cmu1h.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The diagnostic efficiency of multimodal deep learning diagnostic strategy (time frame: The outcome will be measured and analyzed once all the baseline multimodal medical information has been collected.). 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 NCT06081569, which also lists 19 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 13 Oct 2023.