Bibliographic Details
| Title: |
Assessing facial weakness in myasthenia gravis with facial recognition software and deep learning |
| Authors: |
Annabel M. Ruiter, Ziqi Wang, Zhao Yin, Willemijn C. Naber, Jerrel Simons, Jurre T. Blom, Jan C. vanGemert, Jan J. G. M. Verschuuren, Martijn R. Tannemaat |
| Source: |
Annals of Clinical and Translational Neurology, Vol 10, Iss 8, Pp 1314-1325 (2023) |
| Publisher Information: |
Wiley, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Neurosciences. Biological psychiatry. Neuropsychiatry
LCC:Neurology. Diseases of the nervous system |
| Subject Terms: |
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429 |
| More Details: |
Abstract Objective Myasthenia gravis (MG) is an autoimmune disease leading to fatigable muscle weakness. Extra‐ocular and bulbar muscles are most commonly affected. We aimed to investigate whether facial weakness can be quantified automatically and used for diagnosis and disease monitoring. Methods In this cross‐sectional study, we analyzed video recordings of 70 MG patients and 69 healthy controls (HC) with two different methods. Facial weakness was first quantified with facial expression recognition software. Subsequently, a deep learning (DL) computer model was trained for the classification of diagnosis and disease severity using multiple cross‐validations on videos of 50 patients and 50 controls. Results were validated using unseen videos of 20 MG patients and 19 HC. Results Expression of anger (p = 0.026), fear (p = 0.003), and happiness (p |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2328-9503 |
| Relation: |
https://doaj.org/toc/2328-9503 |
| DOI: |
10.1002/acn3.51823 |
| Access URL: |
https://doaj.org/article/a45def69383143acbc1444409491ddd5 |
| Accession Number: |
edsdoj.45def69383143acbc1444409491ddd5 |
| Database: |
Directory of Open Access Journals |
