A multitask AI system demonstrated high accuracy in standardizing and guiding shoulder musculoskeletal ultrasound imaging.
Key Details
- 1Researchers developed a CNN-based AI system to guide acquisition of 15 standard planes and localize 27 structures in shoulder ultrasound.
- 2The model was trained and tested on data from over 13,000 exams and 74,909 images, with external validation on 8,458 images from 480 videos.
- 3In independent external validation, the AI achieved an AUC of 0.99, mean average precision of 0.89, average accuracy of 94%, and F1 scores of 0.87–0.99.
- 4For junior residents, AI-assisted scans reduced exam time by 34% (10.1 min vs 15.3 min; p=0.014).
- 5Independent expert review confirmed the system's guidance accuracy.
- 6Potential applications include tele-ultrasound and patient self-monitoring.
Why It Matters
This study demonstrates that AI can significantly reduce operator dependency and improve standardization in MSK ultrasound, potentially leading to more consistent and accessible imaging. If widely adopted, such AI tools could benefit training, remote imaging, and even patient-led ultrasound acquisition.
Source
AuntMinnie
Related News
•Cardiovascular Business
Radiology Maintains Lead in FDA-Cleared AI Algorithms, Cardiology Follows
Radiology remains the top specialty for FDA-cleared AI, with cardiology as a strong second, particularly in cardiovascular imaging.
•AuntMinnie
Deep Learning Models Rival Radiologists for Pancreatic Cancer Detection on CT
Deep-learning models achieved comparable or superior accuracy to experienced radiologists in detecting pancreatic cancer on CT scans, especially for small tumors.
•Radiology Business
Radiology AI Devices at Elevated Risk for FDA Recalls, Study Finds
Radiology AI devices are more likely to face FDA recalls, largely due to deviations from intended use and incomplete clinical data.