Pose-based tremor type and level analysis for Parkinson's disease from video.
Int J Comput Assist Radiol Surg
; 19(5): 831-840, 2024 May.
Article
en En
| MEDLINE
| ID: mdl-38238490
ABSTRACT
PURPOSE:
Current methods for diagnosis of PD rely on clinical examination. The accuracy of diagnosis ranges between 73 and 84%, and is influenced by the experience of the clinical assessor. Hence, an automatic, effective and interpretable supporting system for PD symptom identification would support clinicians in making more robust PD diagnostic decisions.METHODS:
We propose to analyze Parkinson's tremor (PT) to support the analysis of PD, since PT is one of the most typical symptoms of PD with broad generalizability. To realize the idea, we present SPA-PTA, a deep learning-based PT classification and severity estimation system that takes consumer-grade videos of front-facing humans as input. The core of the system is a novel attention module with a lightweight pyramidal channel-squeezing-fusion architecture that effectively extracts relevant PT information and filters noise. It enhances modeling performance while improving system interpretability.RESULTS:
We validate our system via individual-based leave-one-out cross-validation on two tasks the PT classification task and the tremor severity rating estimation task. Our system presents a 91.3% accuracy and 80.0% F1-score in classifying PT with non-PT class, while providing a 76.4% accuracy and 76.7% F1-score in more complex multiclass tremor rating classification task.CONCLUSION:
Our system offers a cost-effective PT classification and tremor severity estimation results as warning signs of PD for undiagnosed patients with PT symptoms. In addition, it provides a potential solution for supporting PD diagnosis in regions with limited clinical resources.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Enfermedad de Parkinson
/
Temblor
/
Grabación en Video
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Int J Comput Assist Radiol Surg
Asunto de la revista:
RADIOLOGIA
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Alemania