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
Sign In
Advertise with Us

Download Mobile App

Deep Learning Performs As Well As Radiologists in Ultrasonic Liver Analysis

By MedImaging International staff writers
Posted on 12 Oct 2023
Print article
Image: A study has found deep learning to be comparable to radiologists for ultrasonic liver analysis (Photo courtesy of 123RF)
Image: A study has found deep learning to be comparable to radiologists for ultrasonic liver analysis (Photo courtesy of 123RF)

Hepatic steatosis involves the accumulation of fat vacuoles in liver cells and is evaluated through steatosis grades, with higher grades indicating worse health outcomes. Researchers have now discovered that deep learning technologies applied to B-mode ultrasound can categorize these grades just as effectively as human specialists can.

Scientists at Research Center Du Chum (Montreal, Canada) have shown that deep learning matches the capability of radiologists in identifying and grading hepatic steatosis through ultrasound, despite the technology's inherent limitations in imaging fat in the liver. The researchers also highlighted that prior studies haven't sufficiently compared the performance of deep learning with that of human experts using the same test dataset. For their experiment, the team focused on the effectiveness of both radiologists and deep learning algorithms in classifying liver steatosis in patients with nonalcoholic fatty liver disease, using biopsy results as their point of reference.

The researchers employed the VGG16 deep learning model, known for its "moderate" depth and pre-trained capabilities on the ImageNet dataset. They also used fivefold cross-validation during the training phase. The study involved 199 participants, with an average age of 53; 101 were male and 98 were female. The deep learning algorithm exhibited higher AUC (Area Under the Curve) values when distinguishing between steatosis grades of 0 and 1 and performed comparably for higher grades of the condition. A subset of 52 patients was used for this test.

The study also revealed that the agreement among radiologists varied: 0.34 for grades S0 vs. S1 or higher, 0.3 for grades S0 or S1 vs. S2 or S3, and 0.37 for grades S2 or lower vs. S3. In comparison, the deep learning model had significantly higher AUC values in 11 out of 12 readings for the S0 vs. S1 or higher category (p < 0.001). There was no significant difference in the S0 or S1 vs. S2 or S3 range, while for grades S2 or lower vs. S3, the deep learning model outperformed human readings in one instance (P = .002). The researchers concluded that these results warrant further multi-center studies to confirm the efficacy of deep learning models in diagnosing liver steatosis using B-mode ultrasound.

“The performance of our model suggests that deep learning may be used for opportunistic screening of steatosis with use of B-mode ultrasound across scanners from different manufacturers or even for epidemiologic studies at a populational level if deployed on large regional imaging repositories,” stated the researchers.

Related Links:
Research Center Du Chum 

Print article



view channel
Image: Physicians using the Zenition 90 Motorized mobile X-ray system (Photo courtesy of Royal Philips)

High-Powered Motorized Mobile C-Arm Delivers State-Of-The-Art Images for Challenging Procedures

During complex surgical procedures, clinicians depend on surgical imaging systems as they navigate challenging anatomy to quickly visualize small anatomical details while minimizing X-ray exposure.... Read more

Nuclear Medicine

view channel
Image: 68Ga-NC-BCH whole-body PET imaging rapidly targets an important gastrointestinal cancer biomarker in lesions in GI cancer patients (Photo courtesy of Qi, Guo, et al.; doi.org/10.2967/jnumed.123.267110)

New PET Radiotracer Enables Same-Day Imaging of Key Gastrointestinal Cancer Biomarker

Gastrointestinal cancers rank among the most prevalent cancers worldwide, contributing to over a quarter of all cancer cases and over one-third of cancer-related deaths annually. The initial symptoms of... Read more

General/Advanced Imaging

view channel
Image: The denoised image is less noisy and the defect is more detectable and visually clearer with DEMIST (Photo courtesy of Abhinav Jha/WUSTL)

Artificial Intelligence Tool Enhances Usability of Medical Images

Doctors use myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images to evaluate blood flow to the heart muscle. To capture these images, patients are administered a... 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 © 2000-2024 Globetech Media. All rights reserved.