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Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks.
Ye, Run Zhou; Lipatov, Kirill; Diedrich, Daniel; Bhattacharyya, Anirban; Erickson, Bradley J; Pickering, Brian W; Herasevich, Vitaly.
Afiliação
  • Ye RZ; Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.; Division of Endocrinology, Department of Medicine, Centre de Recherche du CHUS, Sherbrooke QC J1H 5N4, Canada.
  • Lipatov K; Critical Care Medicine, Mayo Clinic, Eau Claire, WI, United States.
  • Diedrich D; Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.
  • Bhattacharyya A; Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States.
  • Erickson BJ; Department of Diagnostic Radiology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.
  • Pickering BW; Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.
  • Herasevich V; Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.. Electronic address: vitaly@mayo.edu.
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 AND

METHODS:

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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Síndrome do Desconforto Respiratório / Radiografia Torácica / Redes Neurais de Computação Limite: Adult / Female / Humans / Male / 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

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Síndrome do Desconforto Respiratório / Radiografia Torácica / Redes Neurais de Computação Limite: Adult / Female / Humans / Male / 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