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-Based System Detects Lesions in Knee MR Images

By MedImaging International staff writers
Posted on 10 Aug 2018
Print article
Image: Researchers have developed a deep learning–based system for cartilage lesion detection in knee MR images (Photo courtesy of Health Imaging).
Image: Researchers have developed a deep learning–based system for cartilage lesion detection in knee MR images (Photo courtesy of Health Imaging).
Researchers from the department of radiology at the University of Wisconsin School of Medicine and Public Health (Madison, Wisconsin, USA) have developed a deep learning approach to detect cartilage lesions by evaluating MR images of the knee. The researchers used segmentation and classification convolutional neural networks (CNNs) to develop the fully automated deep learning–based cartilage lesion detection system.

According to the study published in Radiology, the deep learning method was used to retrospectively analyze fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain. The reference standard for training the CNN classification was the interpretation provided by a fellowship-trained musculoskeletal radiologist of the presence or absence of a cartilage lesion within 17,395 small image patches placed on the articular surfaces of the femur and tibia.

In two individual evaluations of the system for the study, the researchers observed a sensitivity of 84.1% and a specificity of 85.2% for evaluation 1, as compared to 80.5% and 87.9%, respectively for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively, indicating high overall diagnostic accuracy for detecting cartilage lesions.

The researchers concluded that the study demonstrated the feasibility of using a fully automated deep learning–based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and that there was good intra-observer agreement for detecting cartilage degeneration and acute cartilage injury.

Related Links:
University of Wisconsin School of Medicine and Public Health

Portable X-ray Unit
AJEX140H
Wall Fixtures
MRI SERIES
Computed Tomography System
Aquilion ONE / INSIGHT Edition
Radiation Shielding
Oversize Thyroid Shield

Print article

Channels

MRI

view channel
Image: The AI tool can help interpret and assess how well treatments are working for MS patients (Photo courtesy of Shutterstock)

AI Tool Tracks Effectiveness of Multiple Sclerosis Treatments Using Brain MRI Scans

Multiple sclerosis (MS) is a condition in which the immune system attacks the brain and spinal cord, leading to impairments in movement, sensation, and cognition. Magnetic Resonance Imaging (MRI) markers... Read more

Nuclear Medicine

view channel
Image: In vivo imaging of U-87 MG xenograft model with varying mass doses of 89Zr-labeled KLG-3 or isotype control (Photo courtesy of L Gajecki et al.; doi.org/10.2967/jnumed.124.268762)

Novel Radiolabeled Antibody Improves Diagnosis and Treatment of Solid Tumors

Interleukin-13 receptor α-2 (IL13Rα2) is a cell surface receptor commonly found in solid tumors such as glioblastoma, melanoma, and breast cancer. It is minimally expressed in normal tissues, making it... 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.