Computerized Facial Recognition for Automated Diagnosis of the Facio-Scapulo-Humeral Muscular Dystrophy (FSMHD)
Computerized Facial Recognition for Automated Diagnosis of the Facio-Scapulo-Humeral Muscular Dystrophy (FSMHD)
The clinical diagnosis of Facio-Scapulo-Humeral Muscular Dystrophy (FSHMD) requires the
 movement of patients to a medical centre and a lengthy examination involving medical
 personnel, and may be underestimated in the most moderate cases. Thus, it requires costly and
 burdensome logistics both for patients living in remote areas and having to undertake long
 and expensive travel, and for clinical staff. This is an obstacle to large-scale diagnosis.
 The investigators plan to alleviate these limitations through the use of digital facial
 analysis technology that would enable large-scale diagnosis of patients through telemedicine.

 Motivated by the reasons described above and by preliminary results, the goal of this project
 is to develop methods to automatically detect and monitor the progression of this disease
 using computer vision algorithms. In order to do this, the investigators will first build up
 a bank of images and videos of patients with moderate to severe FSHMD, patients with other
 muscular dystrophies causing facial muscle asymmetry, as well as control subjects without
 facial involvement. Each of these subjects will be characterized clinically and genetically.

 The investigators will then develop computer tools using video and audio sensors capable of
 detecting facial muscle damage in patients with FSHMD and differentiating them from control
 subjects on the one hand and patients with other muscular dystrophies on the other hand. The
 investigators wish to use the most recent advances in terms of "deep-learning" and improve
 their architecture in order to achieve our objectives.

 In addition to this holistic approach, the investigators will study facial recognition
 approaches capable of accurately identifying different facial areas on images, as well as the
 relevance of different statistical properties of facial dynamics (duration and intensity).
 These algorithms will also be useful for monitoring the evolution of facial damage in order
 to develop a specific measurement tool that could be used in patient follow-up and in
 clinical trials on early stages of the disease.
- Patient belonging to one of these four groups:

 -  Group 1 FSMHD confirmed or to be confirmed with moderate facial involvement

 -  Group 2 FSMHD confirmed or to be confirmed with severe facial involvement.

 -  Group 3 Other disease NM confirmed or to be confirmed.

 -  Group 4 control subjects.

 Exclusion Criteria:

 -  Patient presenting all pathologies judged by the investigator to interfere with the
 smooth running of the study (facial trauma, ...).

 -  Pregnant or breastfeeding women of childbearing age.