Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques
The purpose of this study was to evaluate texture-based muscle ultrasound image analysis for the assessment and risk prediction of frailty phenotype. This retrospective study of prospectively acquired data included 101 participants who underwent ultrasound scanning of the anterior thigh. Participant...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | INCLIVA |
| Repositorio: | r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA |
| OAI Identifier: | oai:incliva.fundanetsuite.com:p17731 |
| Acceso en línea: | https://incliva.portalinvestigacion.com/publicaciones/17731 |
| Access Level: | acceso abierto |
| Palabra clave: | Frailty Muscle Ultrasound Machine-learning Texture analysis Image biomarkers |
| Sumario: | The purpose of this study was to evaluate texture-based muscle ultrasound image analysis for the assessment and risk prediction of frailty phenotype. This retrospective study of prospectively acquired data included 101 participants who underwent ultrasound scanning of the anterior thigh. Participants were subdivided according to frailty phenotype and were followed up for two years. Primary and secondary outcome measures were death and comorbidity, respectively. Forty-three texture features were computed from the rectus femoris and the vastus intermedius muscles using statistical methods. Model performance was evaluated by computing the area under the receiver operating characteristic curve (AUC) while outcome prediction was evaluated using regression analysis. Models developed achieved a moderate to good AUC (0.67 <= AUC <= 0.79) for categorizing frailty. The stepwise multiple logistic regression analysis demonstrated that they correctly classified 70-87% of the cases. The models were associated with increased comorbidity (0.01 <= p <= 0.18) and were predictive of death for pre-frail and frail participants (0.001 <= p <= 0.016). In conclusion, texture analysis can be useful to identify frailty and assess risk prediction (i.e. mortality) using texture features extracted from muscle ultrasound images in combination with a machine learning approach. |
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