End-to-end artificial intelligence platform for the management of large vessel occlusions: A preliminary study.
J Stroke Cerebrovasc Dis
; 31(11): 106753, 2022 Nov.
Article
em En
| MEDLINE
| ID: mdl-36115105
ABSTRACT
OBJECTIVES:
In this study, we developed a deep learning pipeline that detects large vessel occlusion (LVO) and predicts functional outcome based on computed tomography angiography (CTA) images to improve the management of the LVO patients.METHODS:
A series identifier picked out 8650 LVO-protocoled studies from 2015 to 2019 at Rhode Island Hospital with an identified thin axial series that served as the data pool. Data were annotated into 2 classes 1021 LVOs and 7629 normal. The Inception-V1 I3D architecture was applied for LVO detection. For outcome prediction, 323 patients undergoing thrombectomy were selected. A 3D convolution neural network (CNN) was used for outcome prediction (30-day mRS) with CTA volumes and embedded pre-treatment variables as inputs.RESULT:
For LVO-detection model, CTAs from 8,650 patients (median age 68 years, interquartile range (IQR) 58-81; 3934 females) were analyzed. The cross-validated AUC for LVO vs. not was 0.74 (95% CI 0.72-0.75). For the mRS classification model, CTAs from 323 patients (median age 75 years, IQR 63-84; 164 females) were analyzed. The algorithm achieved a test AUC of 0.82 (95% CI 0.79-0.84), sensitivity of 89%, and specificity 66%. The two models were then integrated with hospital infrastructure where CTA was collected in real-time and processed by the model. If LVO was detected, interventionists were notified and provided with predicted clinical outcome information.CONCLUSION:
3D CNNs based on CTA were effective in selecting LVO and predicting LVO mechanical thrombectomy short-term prognosis. End-to-end AI platform allows users to receive immediate prognosis prediction and facilitates clinical workflow.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Isquemia Encefálica
/
Acidente Vascular Cerebral
Tipo de estudo:
Guideline
/
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Female
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Humans
Idioma:
En
Revista:
J Stroke Cerebrovasc Dis
Assunto da revista:
ANGIOLOGIA
/
CEREBRO
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China