A deep learning-based telemonitoring application to automatically assess oral diadochokinesis in patients with bulbar amyotrophic lateral sclerosis.
Comput Methods Programs Biomed
; 242: 107840, 2023 Dec.
Article
en En
| MEDLINE
| ID: mdl-37832429
ABSTRACT
BACKGROUND AND OBJECTIVES:
Timely identification of dysarthria progression in patients with bulbar-onset amyotrophic lateral sclerosis (ALS) is relevant to have a comprehensive assessment of the disease evolution. To this goal literature recognized the utmost importance of the assessment of the number of syllables uttered by a subject during the oral diadochokinesis (DDK) test.METHODS:
To support clinicians, this work proposes a remote deep learning-based system, which consists (i) of a web application to acquire audio tracks of bulbar-onset ALS patients and healthy control subjects while performing the oral DDK test (i.e., repeating the /pa/, /pa-ta-ka/ and /oo-ee/ syllables) and (ii) a DDK-AID network designed to process the acquired audio signals which have different duration and to output the number of per-task syllables repeated by the subject.RESULTS:
The DDK-AID network overcomes the comparative method achieving a mean Accuracy of 90.23 in counting syllables repeated by the eleven bulbar-onset ALS-patients while performing the oral DDK test.CONCLUSIONS:
The proposed remote monitoring system, in the light of the achieved performance, represents an important step towards the implementation of self-service telemedicine systems which may ensure customised care plans.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
/
Esclerosis Amiotrófica Lateral
Límite:
Humans
Idioma:
En
Revista:
Comput Methods Programs Biomed
Asunto de la revista:
INFORMATICA MEDICA
Año:
2023
Tipo del documento:
Article