Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke.
Nat Commun
; 14(1): 4938, 2023 08 15.
Article
en En
| MEDLINE
| ID: mdl-37582829
ABSTRACT
Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial neural network (ANN) which allows automated detection of abnormal vessel findings without any a-priori restrictions and in <2 minutes. Pseudo-prospective external validation was performed in consecutive patients with suspected AIS from 4 different hospitals during a 6-month timeframe and demonstrated high sensitivity (≥87%) and negative predictive value (≥93%). Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25-45% for sensitivity and 4-11% for NPV (p ≤ 0.003 each). We provide an imaging platform ( https//stroke.neuroAI-HD.org ) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Accidente Cerebrovascular
/
Aprendizaje Profundo
/
Accidente Cerebrovascular Isquémico
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Nat Commun
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Alemania