Your browser doesn't support javascript.
Automated Computer Vision Assessment of Hypomimia in Parkinson Disease: Proof-of-Principle Pilot Study.
Abrami, Avner; Gunzler, Steven; Kilbane, Camilla; Ostrand, Rachel; Ho, Bryan; Cecchi, Guillermo.
  • Abrami A; IBM Research - Computational Biology Center, Yorktown Heights, NY, United States.
  • Gunzler S; Parkinson's and Movement Disorders Center, Neurological Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, United States.
  • Kilbane C; Parkinson's and Movement Disorders Center, Neurological Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, United States.
  • Ostrand R; IBM Research - Computational Biology Center, Yorktown Heights, NY, United States.
  • Ho B; Department of Neurology, Tufts Medical Center, Boston, MA, United States.
  • Cecchi G; IBM Research - Computational Biology Center, Yorktown Heights, NY, United States.
J Med Internet Res ; 23(2): e21037, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1256225
ABSTRACT

BACKGROUND:

Facial expressions require the complex coordination of 43 different facial muscles. Parkinson disease (PD) affects facial musculature leading to "hypomimia" or "masked facies."

OBJECTIVE:

We aimed to determine whether modern computer vision techniques can be applied to detect masked facies and quantify drug states in PD.

METHODS:

We trained a convolutional neural network on images extracted from videos of 107 self-identified people with PD, along with 1595 videos of controls, in order to detect PD hypomimia cues. This trained model was applied to clinical interviews of 35 PD patients in their on and off drug motor states, and seven journalist interviews of the actor Alan Alda obtained before and after he was diagnosed with PD.

RESULTS:

The algorithm achieved a test set area under the receiver operating characteristic curve of 0.71 on 54 subjects to detect PD hypomimia, compared to a value of 0.75 for trained neurologists using the United Parkinson Disease Rating Scale-III Facial Expression score. Additionally, the model accuracy to classify the on and off drug states in the clinical samples was 63% (22/35), in contrast to an accuracy of 46% (16/35) when using clinical rater scores. Finally, each of Alan Alda's seven interviews were successfully classified as occurring before (versus after) his diagnosis, with 100% accuracy (7/7).

CONCLUSIONS:

This proof-of-principle pilot study demonstrated that computer vision holds promise as a valuable tool for PD hypomimia and for monitoring a patient's motor state in an objective and noninvasive way, particularly given the increasing importance of telemedicine.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Parkinson Disease / Vision, Ocular Type of study: Experimental Studies / Prognostic study Topics: Long Covid Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 21037

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Parkinson Disease / Vision, Ocular Type of study: Experimental Studies / Prognostic study Topics: Long Covid Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 21037