A deep learning method for predicting knee osteoarthritis radiographic progression from MRI.
Arthritis Res Ther
; 23(1): 262, 2021 10 18.
Article
en En
| MEDLINE
| ID: mdl-34663440
ABSTRACT
BACKGROUND:
The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs.METHODS:
Using 9280 knee magnetic resonance (MR) images (3268 patients) from the Osteoarthritis Initiative (OAI) database , we implemented a deep learning method to predict, from MR images and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months.RESULTS:
Using COR IW TSE images, our classification model achieved a ROC AUC score of 65%. On a similar task, trained radiologists obtained a ROC AUC score of 58.7% highlighting the difficulty of the classification task. Additional analyses conducted in parallel to predict pain grade evaluated by the WOMAC pain index achieved a ROC AUC score of 72%. Attention maps provided evidence for distinct specific areas as being relevant in those two predictive models, including the medial joint space for JSN progression and the intra-articular space for pain prediction.CONCLUSIONS:
This feasibility study demonstrates the interest of deep learning applied to OA, with a potential to support even trained radiologists in the challenging task of identifying patients with a high-risk of disease progression.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Cartílago Articular
/
Osteoartritis de la Rodilla
/
Aprendizaje Profundo
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Arthritis Res Ther
Asunto de la revista:
REUMATOLOGIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Francia