Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy.
Eur Radiol Exp
; 5(1): 7, 2021 02 02.
Artículo
en Inglés
| MEDLINE | ID: covidwho-1059693
ABSTRACT
BACKGROUND:
We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy.METHODS:
We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard.RESULTS:
At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74-0.81), 0.82 specificity (95% CI 0.78-0.85), and 0.89 area under the curve (AUC) (95% CI 0.86-0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72-0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73-0.87), and 0.81 AUC (95% CI 0.73-0.87). Radiologists' reading obtained 0.63 sensitivity (95% CI 0.52-0.74) and 0.78 specificity (95% CI 0.61-0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52-0.74) and 0.86 specificity (95% CI 0.71-0.95) in Centre 2.CONCLUSIONS:
This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Rayos X
/
Interpretación de Imagen Radiográfica Asistida por Computador
/
Aprendizaje Automático
/
COVID-19
Tipo de estudio:
Estudio de cohorte
/
Estudios diagnósticos
/
Estudio observacional
/
Estudio pronóstico
/
Ensayo controlado aleatorizado
Límite:
Anciano
/
Femenino
/
Humanos
/
Masculino
/
Middle aged
País/Región como asunto:
Europa
Idioma:
Inglés
Revista:
Eur Radiol Exp
Año:
2021
Tipo del documento:
Artículo
País de afiliación:
S41747-020-00203-z
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