AI-Rad Companion (Musculoskeletal)
Siemens Medical Solutions USA, Inc.AI-Rad Companion (Musculoskeletal) is an AI-powered software that analyzes previously acquired CT images to assist radiologists and physicians in evaluating musculoskeletal diseases. It segments, labels vertebrae, measures vertebral heights, and computes mean Hounsfield values to provide detailed quantitative and qualitative analysis, improving assessment and workflow efficiency for clinicians.
Intended Use
Image processing software providing quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support radiologists and physicians in evaluation and assessment of musculoskeletal disease, including vertebra segmentation, labeling, vertebral height measurement, and mean Hounsfield value measurement within vertebrae in adult patients.
Technology
Software utilizes deep learning algorithms for vertebra segmentation and labeling on CT DICOM images. Provides quantitative measurements of vertebral height and mean Hounsfield value. Supports cloud and on-premise (edge) deployment. Generates enhanced human and machine-readable DICOM structured reports compliant with TID 1500 standard. Maintains adherence to FDA cybersecurity guidance and relevant FDA-recognized standards for software lifecycle, risk management, and usability.
Performance
Performance testing included software validation and bench testing on 140 clinically relevant chest CT datasets with thoracic vertebrae, evaluating segmentation accuracy, vertebral height measurements, and mean density (HU). Mislabeling failure rate was 8.6%, and height measurement differences met clinical limits of agreement in over 92-95% of cases depending on slice thickness. Testing showed performance equivalent or superior to predicate device. Cybersecurity controls were verified per FDA guidance.
Predicate Devices
No predicate devices specified
Device Timeline
- 1
Submission
8/4/2022
2 months - 2
FDA Approval
10/20/2022
Other devices from Siemens Medical Solutions USA, Inc.
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