The Face of Neuromuscular Dysfunction: Artificial Intelligence for the Analysis of Video Data of Facial Movement, with a Focus on Myasthenia Gravis
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 90
- 试验地点
- 1
- 主要终点
- FaceReader
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Male or female participants aged ≥ 18 years
- •Subjects must understand the requirements of the study and provide written informed consent.
- •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
- •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
排除标准
- •Inability to give written informed consent
- •Inability to read Dutch/ English video-instructions
- •Participants with active Graves' disease or unilateral facial paralysis
结局指标
主要结局
FaceReader
时间窗: 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
时间窗: 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
时间窗: 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.
次要结局
- Longitudinal changes(2020-2023)
- FaceReader vs deep learning model(2020-2023)
- site versus home recording(2020-2023)
研究者
Martijn R. Tannemaat, MD PhD
MD PhD
Leiden University Medical Center