Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks.
J Crit Care
; 82: 154794, 2024 Aug.
Article
em En
| MEDLINE
| ID: mdl-38552452
ABSTRACT
OBJECTIVE:
This study aims to design, validate and assess the accuracy a deep learning model capable of differentiation Chest X-Rays between pneumonia, acute respiratory distress syndrome (ARDS) and normal lungs. MATERIALS ANDMETHODS:
A diagnostic performance study was conducted using Chest X-Ray images from adult patients admitted to a medical intensive care unit between January 2003 and November 2014. X-ray images from 15,899 patients were assigned one of three prespecified categories "ARDS", "Pneumonia", or "Normal".RESULTS:
A two-step convolutional neural network (CNN) pipeline was developed and tested to distinguish between the three patterns with sensitivity ranging from 91.8% to 97.8% and specificity ranging from 96.6% to 98.8%. The CNN model was validated with a sensitivity of 96.3% and specificity of 96.6% using a previous dataset of patients with Acute Lung Injury (ALI)/ARDS.DISCUSSION:
The results suggest that a deep learning model based on chest x-ray pattern recognition can be a useful tool in distinguishing patients with ARDS from patients with normal lungs, providing faster results than digital surveillance tools based on text reports.CONCLUSION:
A CNN-based deep learning model showed clinically significant performance, providing potential for faster ARDS identification. Future research should prospectively evaluate these tools in a clinical setting.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Síndrome do Desconforto Respiratório
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Radiografia Torácica
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Redes Neurais de Computação
Limite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
J Crit Care
Assunto da revista:
TERAPIA INTENSIVA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Canadá