We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




Deep Learning Model Accurately Predicts Pneumonia Mortality on Chest X-Rays

By MedImaging International staff writers
Posted on 23 Jun 2023
Image: Deep learning could better guide clinical decision-making in patients with pneumonia (Photo courtesy of Freepik)
Image: Deep learning could better guide clinical decision-making in patients with pneumonia (Photo courtesy of Freepik)

Chest X-rays are a crucial diagnostic tool for community-acquired pneumonia (CAP), despite their uncertain prognostic value. Now, a deep learning (DL)-based model that utilizes initial chest X-rays has shown potential in accurately predicting 30-day mortality, outperforming the well-established risk prediction tool, the CURB-65 score.

Researchers at Seoul National University (Seoul, Korea) have created a DL model using data from 7,105 patients from one institution, gathered between March 2013 and December 2019. This data was used to form training, validation, and internal testing sets to predict the risk of all-cause mortality within 30 days post-CAP diagnosis using patients' initial chest radiographs. The researchers then evaluated the DL model in two test cohorts: 947 patients diagnosed with CAP during emergency department visits at the original institution between January 2020 and December 2020, and 848 additional patients from two separate institutions from January 2020 to December 2020 and March 2019 to October 2021. The study compared the performance of the DL model with a risk score based on confusion, blood urea nitrogen level, respiratory rate, blood pressure, and age ≥ 65 years (CURB-65 score).

The results demonstrated that the DL model, using initial chest radiographs, could predict 30-day, all-cause mortality in patients with CAP with an area under the curve (AUC) between 0.77 and 0.80 in the different test cohorts. Moreover, the model demonstrated higher specificity (61–69% range) than the CURB-65 score (44–58% range) at the same sensitivity level. Given these results, the researchers suggest that this DL-based model could better assist clinicians in decision-making when managing patients with CAP.

“The deep learning (DL) model may guide clinical decision-making in the management of patients with CAP by identifying high-risk patients who warrant hospitalization and intensive treatment,” said Eui Jin Hwang, MD, PhD, from the Department of Radiology at Seoul National University College of Medicine.

Related Links:
Seoul National University 

Digital Intelligent Ferromagnetic Detector
Digital Ferromagnetic Detector
New
MRI System
nanoScan MRI 3T/7T
New
Mobile X-Ray System
K4W
Pocket Fetal Doppler
CONTEC10C/CL

Channels

Nuclear Medicine

view channel
Image: Perovskite crystal boules are grown in carefully controlled conditions from the melt (Photo courtesy of Mercouri Kanatzidis/Northwestern University)

New Camera Sees Inside Human Body for Enhanced Scanning and Diagnosis

Nuclear medicine scans like single-photon emission computed tomography (SPECT) allow doctors to observe heart function, track blood flow, and detect hidden diseases. However, current detectors are either... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.