Myasthenia GravisNCT06860360
Artificial Intelligence for the Analysis of Video Data of Facial Movement, with a Focus on Myasthenia Gravis
An observational study of Myasthenia Gravis, run by Leiden University Medical Center at 1 site in Netherlands.
Trial summary
Rationale: Myasthenia Gravis (MG) is an autoimmune disorder (AID) with antibodies against the NMJ, resulting in various degrees of muscle fatigability and weakness. All striated muscles can be involved, although the extra-ocular muscles are most commonly affected, giving rise to a fluctuating ptosis and diplopia. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Myasthenia Gravis
- How many people
- 90 participants
- Where
- 1 siteNetherlands
- Study dates
- 1 Dec 2020to 31 Dec 2022
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: To answer the main objectives we will obtain a 04:00 min standardized facial video recording of MG patients in the outpatient clinic and Neurology ward, recorded at the LUMC. For the webcam recordings we will recruit participants from the Dutch-Belgian MG registry (P15.287). Informed consent forms of the MG registry with permission to be approached for future research are available. We will ask spouses and family to enrol as healthy controls, as well as employees of our department. The healthy control group will be age- and gender matched. Healthy controls are defined as subjects without medical conditions or medication which may influence the facial muscles, e.g. Graves thyroid disease, previous facial palsy due to a stroke or prednisone use. For the webcam recordings we will ask MG patients to introduce a healthy control of the same age.
Who may be able to join
- Male or female participants aged ≥ 18 years
- Subjects must understand the requirements of the study and provide written informed consent.
- MG
- Clinical signs or symptoms suggestive of MG and at least one of the following:
- A serologic test for AChR antibodies or MuSK antibodies or
- A diagnostic electrophysiological investigation supportive of the diagnosis MG or
Show all 10Show fewer
- A positive neostigmine test Healthy control group
- Volunteers from spouses, friends and family accompanying patient or employees from our department
- No medical conditions affecting the facial muscles, e.g. Graves' disease, previous stroke with a facial palsy
- No use of medication affecting the facial features, e.g. prednisone
Who cannot join
- Inability to give written informed consent
- Inability to read Dutch/ English video-instructions
- Participants with active Graves' disease or unilateral facial paralysis
Trial locations and contacts
1 site in 1 country.
Netherlands1 site
Leiden University Medical Center
Leiden, South Holland, 2333ZA
About this study
Official title: The Face of Neuromuscular Dysfunction: Artificial Intelligence for the Analysis of Video Data of Facial Movement, with a Focus on Myasthenia Gravis
Rationale: Myasthenia Gravis (MG) is an autoimmune disorder (AID) with antibodies against the NMJ, resulting in various degrees of muscle fatigability and weakness. All striated muscles can be involved, although the extra-ocular muscles are most commonly affected, giving rise to a fluctuating ptosis and diplopia. Facial muscles are also commonly affected, resulting in eye closure weakness, difficulty chewing and swallowing or speech impairments. Antibodies against the acetylcholine receptor (AChR) are present in over 80% of generalized MG patients. In the pure ocular form, AChR antibodies are detectable in nearly 50% of all patients. In approximately 4%, antibodies against the postsynaptic muscle-specific receptor tyrosine kinase (MuSK) are found and in 15% of the patients with generalized disease, no serum antibodies are detected1-3. Approximately 15% of AChR MG patients has a thymoma, in which case the disease can be classified as a paraneoplastic syndrome2. With a prevalence of 1 to 2 per 10.000, MG is considered a rare disease2.
The rarity of MG can make it difficult to diagnose, specifically for general Neurologists who are likely to encounter a patient with MG only a handful of times throughout their career. In addition, the fluctuating nature of the disease makes it difficult to make appropriate treatment decisions, especially as patients throughout the country are usually treated at one specialized center (in the Netherlands, the LUMC). Currently, patients who are in doubt whether they are experiencing an exacerbation have to make an appointment and travel for several hours to undergo assessment by their specialized Neurologist. An objective, reliable biomarker for disease severity that can be used at home would therefore greatly improve quality of life for many MG patients. Emerging possibilities in modern technologies can support doctors with all kinds of medical challenges, like offering diagnostic support, treatment decisions or patient follow-up. A technology of special interest for this study is advanced facial recognition. We aim to study the ability of existing software (FaceReader, Noldus) versus a deep learning model specifically developed for this purpose by the group of Jan van Gemert at the TU Delft to differentiate between healthy controls and patients with MG and between MG patients with different levels of disease severity.
Read more
Primary objectives:
To determine and compare the diagnostic yield of two different methods (FaceReader technology and a deep learning model specifically developed for video data) to analyse facial weakness from video recordings (04:00m) with different standardized facial expressions to:
- Differentiate between MG patients and healthy controls.
- Differentiate between mild and moderate to severe disease severity.
- Conditions
- Myasthenia Gravis
- Medical subject headings
- Myasthenia Gravis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| myasthenia gravis | Not described | |
| healthy controls | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
FaceReader
The primary parameter for the differentiation between healthy controls and MG and between different grades of MG disease severity with FaceReader is the diagnostic yield of individual muscles and combinations of muscles. The diagnostic yield is expressed as sensitivity, specificity and area under the curve of a receiver-operator curve (ROC) of the FaceReader algorithm. For this the quantitative data of facial weakness expressed in Action Units (AU), ranging between 0 (no activation) and +1 (maximal activation) will be used. Raw data from FaceReader provides the results of 20 AU's corresponding with 20 different facial movements of 20 facial muscles based on the Facial Action Coding System (FACS).
Measured: 2020-2023
Narrow deep learning model
The primary parameter for the differentiation between healthy controls and MG and between different grades of MG disease severity with a working narrow deep learning model is the diagnostic yield. The diagnostic yield is expressed as sensitivity, specificity and area under the curve of a receiver-operator curve (ROC).
Measured: 2020-2023
Disease severity
For comparison between different levels of disease severity the QMG score will serve as the gold standard. Groups based on disease severity are composed as following: mild QMG 0-9, moderate QMG 10-16 and severe QMG >16. For the home recording we will use the MG-Activities of Daily Living (MG-ADL) to measure disease severity since the QMG requires the physical presence of the patient. This is a commonly used tool in clinical trials. Groups based on disease severity are composed as following: mild MG-ADL 0-4, moderate-severe MG-ADL ≥5.
Measured: 2020-2023
Secondary outcomes (3)
Longitudinal changes
Detection of medication effects by obtaining multiple videos (longitudinal) in a subset of patients. The QMG score or MG-ADL is the parameter for change in disease severity. A previous study found a minimal clinically important difference (MCID) in QMG score of ≥2 for a baseline QMG score between 0 and 16. For a baseline QMG score >16 the MCID is ≥3 points change in QMG score4. For the MG-ADL the MCID is a change of ≥2 points5. For detection of medication effects, our aim is to detect an intra-participant change in QMG ≥2 or ≥3, depending of baseline QMG score. For the home recorded group our aim is to detect a change in MG-ADL score of ≥2 points.
Measured: 2020-2023
FaceReader vs deep learning model
A comparison of the diagnostic yield of FaceReader parameters and classification by the deep learning model.
Measured: 2020-2023
site versus home recording
A comparison of the diagnostic yield of FaceReader and the deep learning model of videos recorded in the standardized LUMC setting (green screen, lights, 4K camera) and home recorded videos using a webcam.
Measured: 2020-2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Mar 2025
- Study start1 Dec 2020
- Primary completion31 Dec 2022
- Study completion31 Dec 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Myasthenia GravisStudies of the same condition, open and under way.
- More trials run by Leiden University Medical CenterThe 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
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 90 participants (actual)
Sponsor and investigators
- Lead sponsor
- Leiden University Medical Center
- Sponsor type
- University, hospital or other
- Collaborators
- Delft University of Technology
- Responsible party
- Principal investigator, Martijn R. Tannemaat, MD PhD, Leiden University Medical Center
- Investigators
- Martijn Tannemaat, MD, PhD, Principal investigator, Leiden University Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06860360
- Sponsor's protocol ID
- P20.083
- Status last verified
- Mar 2025
- First submitted
- 3 Aug 2022
- Last updated
- 6 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Raw data, including de-identified individual participants' data, that support the findings of this study are available from the corresponding author, upon reasonable request. Privacy laws preclude the sharing of facial videos of individual study participants.
Study at a glance
From the registry record, last updated by the sponsor on 6 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Face of Neuromuscular Dysfunction: Artificial Intelligence for the Analysis of Video Data of Facial Movement, with a Focus on Myasthenia Gravis
- ClinicalTrials.gov ID
- NCT06860360
- Acronym / sponsor ID
- P20.083
- Conditions
- Myasthenia Gravis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 90 participants (actual)
- Lead sponsor
- Leiden University Medical CenterUniversity, hospital or other
- Collaborators
- Delft University of Technology
- Study start
- 1 Dec 2020 (actual)
- Primary completion
- 31 Dec 2022 (actual)
- Study completion
- 31 Dec 2022 (actual)
- First posted
- 6 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.
Rationale: Myasthenia Gravis (MG) is an autoimmune disorder (AID) with antibodies against the NMJ, resulting in various degrees of muscle fatigability and weakness. All striated muscles can be involved, although the extra-ocular muscles are most commonly affected, giving rise to a fluctuating ptosis and diplopia. Facial muscles are also commonly affected, resulting in eye closure weakness, difficulty chewing and swallowing or speech impairments. Antibodies against the acetylcholine receptor (AChR) are present in over 80% of generalized MG patients. In the pure ocular form, AChR antibodies are detectable in nearly 50% of all patients. In approximately 4%, antibodies against the postsynaptic muscle-specific receptor tyrosine kinase (MuSK) are found and in 15% of the patients with generalized disease, no serum antibodies are detected1-3. Approximately 15% of AChR MG patients has a thymoma, in which case the disease can be classified as a paraneoplastic syndrome2. With a prevalence of 1 to 2 per 10.000, MG is considered a rare disease2.
The rarity of MG can make it difficult to diagnose, specifically for general Neurologists who are likely to encounter a patient with MG only a handful of times throughout their career. In addition, the fluctuating nature of the disease makes it difficult to make appropriate treatment decisions, especially as patients throughout the country are usually treated at one specialized center (in the Netherlands, the LUMC). Currently, patients who are in doubt whether they are experiencing an exacerbation have to make an appointment and travel for several hours to undergo assessment by their specialized Neurologist. An objective, reliable biomarker for disease severity that can be used at home would therefore greatly improve quality of life for many MG patients. Emerging possibilities in modern technologies can support doctors with all kinds of medical challenges, like offering diagnostic support, treatment decisions or patient follow-up. A technology of special interest for this study is advanced facial recognition. We aim to study the ability of existing software (FaceReader, Noldus) versus a deep learning model specifically developed for this purpose by the group of Jan van Gemert at the TU Delft to differentiate between healthy controls and patients with MG and between MG patients with different levels of disease severity.
Primary objectives:
To determine and compare the diagnostic yield of two different methods (FaceReader technology and a deep learning model specifically developed for video data) to analyse facial weakness from video recordings (04:00m) with different standardized facial expressions to:
- Differentiate between MG patients and healthy controls.
- Differentiate between mild and moderate to severe disease severity.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| myasthenia gravis | – | – |
| healthy controls | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- To answer the main objectives we will obtain a 04:00 min standardized facial video recording of MG patients in the outpatient clinic and Neurology ward, recorded at the LUMC. For the webcam recordings we will recruit participants from the Dutch-Belgian MG registry (P15.287). Informed consent forms of the MG registry with permission to be approached for future research are available. We will ask spouses and family to enrol as healthy controls, as well as employees of our department. The healthy control group will be age- and gender matched. Healthy controls are defined as subjects without medical conditions or medication which may influence the facial muscles, e.g. Graves thyroid disease, previous facial palsy due to a stroke or prednisone use. For the webcam recordings we will ask MG patients to introduce a healthy control of the same age.
Key inclusion criteria (10 listed)
- Male or female participants aged ≥ 18 years
- Subjects must understand the requirements of the study and provide written informed consent.
- MG
- Clinical signs or symptoms suggestive of MG and at least one of the following:
- A serologic test for AChR antibodies or MuSK antibodies or
Key exclusion criteria (3 listed)
- Inability to give written informed consent
- Inability to read Dutch/ English video-instructions
- Participants with active Graves' disease or unilateral facial paralysis
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
FaceReader
Time frame: 2020-2023
The primary parameter for the differentiation between healthy controls and MG and between different grades of MG disease severity with FaceReader is the diagnostic yield of individual muscles and combinations of muscles. The diagnostic yield is expressed as sensitivity, specificity and area under the curve of a receiver-operator curve (ROC) of the FaceReader algorithm. For this the quantitative data of facial weakness expressed in Action Units (AU), ranging between 0 (no activation) and +1 (maximal activation) will be used. Raw data from FaceReader provides the results of 20 AU's corresponding with 20 different facial movements of 20 facial muscles based on the Facial Action Coding System (FACS).
Narrow deep learning model
Time frame: 2020-2023
The primary parameter for the differentiation between healthy controls and MG and between different grades of MG disease severity with a working narrow deep learning model is the diagnostic yield. The diagnostic yield is expressed as sensitivity, specificity and area under the curve of a receiver-operator curve (ROC).
Disease severity
Time frame: 2020-2023
For comparison between different levels of disease severity the QMG score will serve as the gold standard. Groups based on disease severity are composed as following: mild QMG 0-9, moderate QMG 10-16 and severe QMG >16. For the home recording we will use the MG-Activities of Daily Living (MG-ADL) to measure disease severity since the QMG requires the physical presence of the patient. This is a commonly used tool in clinical trials. Groups based on disease severity are composed as following: mild MG-ADL 0-4, moderate-severe MG-ADL ≥5.
3 secondary outcomes
Longitudinal changes
Time frame: 2020-2023
FaceReader vs deep learning model
Time frame: 2020-2023
site versus home recording
Time frame: 2020-2023
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Mar 2025.
- Individual participant dataSharing planned: Yes. Raw data, including de-identified individual participants' data, that support the findings of this study are available from the corresponding author, upon reasonable request. Privacy laws preclude the sharing of facial videos of individual study participants.
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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.
Martijn Tannemaat, MD, PhD principal investigator
Leiden University Medical Center
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 Myasthenia Gravis, run by Leiden University Medical Center at 1 site in Netherlands.
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 10 inclusion and 3 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: finished, as of the sponsor's last update on 6 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 Netherlands. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
FaceReader (time frame: 2020-2023). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 6 Mar 2025.