The application of machine learning methods for predicting the progression of adolescent idiopathic scoliosis: a systematic review.
Biomed Eng Online
; 23(1): 80, 2024 Aug 08.
Article
en En
| MEDLINE
| ID: mdl-39118179
ABSTRACT
Predicting curve progression during the initial visit is pivotal in the disease management of patients with adolescent idiopathic scoliosis (AIS)-identifying patients at high risk of progression is essential for timely and proactive interventions. Both radiological and clinical factors have been investigated as predictors of curve progression. With the evolution of machine learning technologies, the integration of multidimensional information now enables precise predictions of curve progression. This review focuses on the application of machine learning methods to predict AIS curve progression, analyzing 15 selected studies that utilize various machine learning models and the risk factors employed for predictions. Key findings indicate that machine learning models can provide higher precision in predictions compared to traditional methods, and their implementation could lead to more personalized patient management. However, due to the model interpretability and data complexity, more comprehensive and multi-center studies are needed to transition from research to clinical practice.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Escoliosis
/
Progresión de la Enfermedad
/
Aprendizaje Automático
Límite:
Adolescent
/
Humans
Idioma:
En
Revista:
Biomed Eng Online
Asunto de la revista:
ENGENHARIA BIOMEDICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
China