Deep learning-based personalised outcome prediction after acute ischaemic stroke.
J Neurol Neurosurg Psychiatry
; 94(5): 369-378, 2023 05.
Article
en En
| MEDLINE
| ID: mdl-36650037
ABSTRACT
BACKGROUND:
Whether deep learning models using clinical data and brain imaging can predict the long-term risk of major adverse cerebro/cardiovascular events (MACE) after acute ischaemic stroke (AIS) at the individual level has not yet been studied.METHODS:
A total of 8590 patients with AIS admitted within 5 days of symptom onset were enrolled. The primary outcome was the occurrence of MACEs (a composite of stroke, acute myocardial infarction or death) over 12 months. The performance of deep learning models (DeepSurv and Deep-Survival-Machines (DeepSM)) and traditional survival models (Cox proportional hazards (CoxPH) and random survival forest (RSF)) were compared using the time-dependent concordance index ([Formula see text] index).RESULTS:
Given the top 1 to all 60 clinical factors according to feature importance, CoxPH and RSF yielded [Formula see text] index of 0.7236-0.8222 and 0.7279-0.8335, respectively. Adding image features improved the performance of deep learning models and traditional models assisted by deep learning models. DeepSurv and DeepSM yielded the best [Formula see text] index of 0.8496 and 0.8531 when images were added to all 39 relevant clinical factors, respectively. In feature importance, brain image was consistently ranked highly. Deep learning models automatically extracted the image features directly from personalised brain images and predicted the risk and date of future MACEs at the individual level.CONCLUSIONS:
Deep learning models using clinical data and brain images could improve the prediction of MACEs and provide personalised outcome prediction for patients with AIS. Deep learning models will allow us to develop more accurate and tailored prognostic prediction systems that outperform traditional models.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Isquemia Encefálica
/
Accidente Cerebrovascular
/
Aprendizaje Profundo
/
Accidente Cerebrovascular Isquémico
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
J Neurol Neurosurg Psychiatry
Año:
2023
Tipo del documento:
Article