Clinical trial · Observational
Help Build an A.I. Model to Predict Myasthenia Gravis Symptom Patterns and Flares
A Digital Health Trial That Assesses Participant-driven Data Collection Using Smartphone Modules to Characterize Myasthenia Gravis Symptoms and Develop an A.I. Model to Predict Flares
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
There are limited objective measurements of MG symptoms as well as a dearth of data at a granular level of MG (myasthenia gravis) symptoms and triggers occurring longitudinally. This study is designed to use the strengths of mobile smartphones which enable participant-driven real time capture of data manually and through augmented sensors such as video and audio, in order to better characterize MG symptoms and flares. The study aims to enroll approximately 200 participants for approximately 9 months until analyzable data is available from at least 100 participants. Participants will complete in-app surveys for 3 months with, audiovisual recording of symptoms. This will take approximately 35 minutes per week after the initial survey.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Myasthenia Gravis | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Data Collection | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Audiovisual recording of voice exercises to detect patterns and changes in voice and facial symptoms
- timeFrame
- After enrollment, 3 months with in-app twice a week audiovisual recording of symptoms.
- description
- participants to complete the audio and visual data modules designed to capture patient MG symptoms (especially ocular and voice). e.g * Vocal e.g.: * Say "papapapa" for 4 seconds * Say "tatatatata" for 4 seconds * Say "kakakaka" 4 seconds * Say "mamamama" 4 seconds * Say "papapapa" 4 seconds * Say "buttercup, buttercup, buttercup" 4 seconds * Say "aaaahhh" and hold it as long as you can * Counting e.g.: * Look straight at the camera for 4 seconds * Count as precisely as possible from 1 to 25 while looking up * Look straight at the camera for 4 seconds The recordings will be used to detect change from baseline and any patterns that may occur. This will be used to analyze where and if different features are linked to see if a single or combined effect of the features is connected to flare frequency and/or severity.
Secondary outcomes (1)
- measure
- Completion of MG-Quality of Life assessment
- timeFrame
- Approximately 10 minutes each week for 3 months.
- description
- Participants complete MG activities of Daily living and MG-Quality of Life assessments weekly. This assessment has been adapted from www.myasthenia.org/healthprofessionals/educationalmaterials.aspx
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: 1. Must have a documented diagnosis of Myasthenia Gravis 2. Must have ocular (eye drooping) and/or bulbar (speech) symptoms 3. Must be over the age of 18 4. Must reside in the US for the duration of the study 5. Must be able to read, understand, and write in English 6. Must have a smartphone supported by the doc.ai research app (iOS and Android) Exclusion Criteria: None
References
Publications (11)
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- RESULTZhou ZR, Wang WW, Li Y, Jin KR, Wang XY, Wang ZW, Chen YS, Wang SJ, Hu J, Zhang HN, Huang P, Zhao GZ, Chen XX, Li B, Zhang TS. In-depth mining of clinical data: the construction of clinical prediction model with R. Ann Transl Med. 2019 Dec;7(23):796. doi: 10.21037/atm.2019.08.63. PMID 32042812
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- RESULTBorza D, Darabant AS, Danescu R. Real-Time Detection and Measurement of Eye Features from Color Images. Sensors (Basel). 2016 Jul 16;16(7):1105. doi: 10.3390/s16071105. PMID 27438838
- RESULTHegde S, Shetty S, Rai S, Dodderi T. A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders. J Voice. 2019 Nov;33(6):947.e11-947.e33. doi: 10.1016/j.jvoice.2018.07.014. Epub 2018 Oct 11. PMID 30316551
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- RESULTDuffy, JR: Motor Speech Disorders. Substrates, Differential Diagnosis and Management (2nd ed). New York, 2005, Elsevier Health Sciences.
- RESULTT. Baltrusaitis, A. Zadeh, Y. C. Lim and L. Morency,