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

Download Mobile App




Artificial Intelligence Helps Radiologists Improve Chest X-Ray Interpretation, Finds New Study

By MedImaging International staff writers
Posted on 05 Jul 2021
Illustration
Illustration
A new diagnostic accuracy study has shown that radiologists can better interpret chest X-rays when assisted by a comprehensive deep-learning model that had a similar or better accuracy than the radiologists for most findings when compared with high-quality, gold standard assessment techniques.

Chest X-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest X-ray interpretation. Therefore, the researchers aimed to assess the accuracy of radiologists with and without the assistance of a deep-learning model.

In the retrospective study, a deep-learning model was trained on 821,681 images (284,649 patients) from five data sets from Australia, Europe, and the US. 2,568 enriched chest X-ray cases from adult patients who had at least one frontal chest X-ray were included in the test dataset; cases were representative of inpatient, outpatient, and emergency settings. 20 radiologists reviewed cases with and without the assistance of the deep-learning model with a three-month washout period. The researchers assessed the change in accuracy of chest X-ray interpretation across 127 clinical findings when the deep-learning model was used as a decision support by calculating area under the receiver operating characteristic curve (AUC) for each radiologist with and without the deep-learning model. The team also compared AUCs for the model alone with those of unassisted radiologists. If the lower bound of the adjusted 95% CI of the difference in AUC between the model and the unassisted radiologists was more than −0·05, the model was considered to be non-inferior for that finding. If the lower bound exceeded 0, the model was considered to be superior.

The researchers found that unassisted radiologists had a macroaveraged AUC of 0·713 (95% CI 0·645–0·785) across the 127 clinical findings, compared with 0·808 (0·763–0·839) when assisted by the model. The deep-learning model statistically significantly improved the classification accuracy of radiologists for 102 (80%) of 127 clinical findings, was statistically non-inferior for 19 (15%) findings, and no findings showed a decrease in accuracy when radiologists used the deep-learning model. Unassisted radiologists had a macroaveraged mean AUC of 0·713 (0·645–0·785) across all findings, compared with 0·957 (0·954–0·959) for the model alone. Model classification alone was significantly more accurate than unassisted radiologists for 117 (94%) of 124 clinical findings predicted by the model and was non-inferior to unassisted radiologists for all other clinical findings. Thus, the study demonstrated the potential of a comprehensive deep-learning model to improve chest X-ray interpretation across a large breadth of clinical practice.


Breast Localization System
MAMMOREP LOOP
Digital Intelligent Ferromagnetic Detector
Digital Ferromagnetic Detector
Digital Radiographic System
OMNERA 300M
Ultrasonic Pocket Doppler
SD1

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

General/Advanced Imaging

view channel
Image: The Angio-CT solution integrates the latest advances in interventional imaging (Photo courtesy of Canon Medical)

Cutting-Edge Angio-CT Solution Offers New Therapeutic Possibilities

Maintaining accuracy and safety in interventional radiology is a constant challenge, especially as complex procedures require both high precision and efficiency. Traditional setups often involve multiple... 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.