Automatic diagnosis of vocal fold paresis by employing phonovibrogram features and machine learning methods.
The clinical diagnosis of voice disorders is based on examination of the rapidly moving vocal folds during phonation (f0: 80-300Hz) with state-of-the-art endoscopic high-speed cameras. Commonly, analysis is performed in a subjective and time-consuming manner via slow-motion video playback and exhibits low inter- and intra-rater reliability. In this study an objective method to overcome this drawback is presented being based on Phonovibrography, a novel image analysis technique. For a collective of 45 normophonic and paralytic voices the laryngeal dynamics were captured by specialized Phonovibrogram features and analyzed with different machine learning algorithms. Classification accuracies reached 93% for 2-class and 73% for 3-class discrimination. The results were validated by subjective expert ratings given the same diagnostic criteria. The automatic Phonovibrogram analysis approach exceeded the experienced raters' classifications by 9%. The presented method holds a lot of potential for providing reliable vocal fold diagnosis support in the future.
Department of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Bohlenplatz 21, D-91054 Erlangen, Germany. daniel.voigt@uk-erlangen.de
Daniel
D
Voigt
Michael
M
Döllinger
Anxiong
A
Yang
Ulrich
U
Eysholdt
Jörg
J
Lohscheller
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D007828:Laryngoscopy / Q000295:instrumentation*; D010363:Pattern Recognition, Automated; D010699:Phonation; D014741:Video Recording / Q000295:instrumentation / Q000379:methods; D014826:Vocal Cord Paralysis / Q000175:diagnosis* / Q000473:pathology; D014827:Vocal Cords / Q000473:pathology*