AI model aids in ultrasound rotator cuff exams

An AI assistance system can aid novice sonographers in assessing the rotator cuff, a study published December 1 in Ultrasound in Medicine & Biology found. Researchers led by Rui Tang from Peking University Third Hospital in Beijing, China, found that their AI system can accurately identify standard planes and perform automatic tissue segmentation. “Remarkably, the system demonstrated superior performance compared with similar studies for both functions,” Tang and colleagues wrote. While ultrasound is a go-to method for assessing shoulder joint diseases, it is a user-dependent modality. Novice ultrasonographers may struggle with imaging the shoulder since they need a large understanding of the anatomical distribution characteristics of local structures within the shoulder region. Previous studies have demonstrated the use of deep learning in diagnosing muscle status using CT, MRI, and ultrasound images to detect rotator cuff tears. Tang and co-authors tested the performance of an AI assistance system for shoulder ultrasound imaging. They developed the system by using a standard plane recognition module based on the ResNet50 network and an automatic tissue segmentation module using the Mask R-CNN model. The team used a dedicated data set of shoulder joint ultrasound images to assess the model’s use in clinical practice. The researchers found that the standard plane recognition model, which used 59,265 ultrasound images, achieved a recognition accuracy of 94.9% in th...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Subspecialties Ultrasound Musculoskeletal Radiology Source Type: news