brAIn Shoulder Positioning
K252665Avatar Medical · cleared 2025-10-20 · product code QIH · Radiology
Premarket evidence — what FDA accepted
source quote (p.7)
“The braln™ Shoulder Positioning software is a cloud-based application intended for shoulder surgeons. The software automatically segments (using machine learning) and performs measurements on the scapula and humerus anatomy contained in the DICOM series.”
source quote (p.7)
“The software automatically segments (using machine learning) and performs measurements on the scapula and humerus anatomy contained in the DICOM series.”
source quote (p.10)
“Additionally, the software validation activities were performed in accordance with ANSI AAMI IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, in addition to the FDA Guidance documents, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" and "Content of Premarket Submission for Management of Cybersecurity in Medical Devices."”
Validation studies (6)
Bench
sample size not stated
standards: ANSI AAMI IEC 62304:2006/A1:2016 - Medical device software – Software life cycle processes, Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices, Content of Premarket Submission for Management of Cybersecurity in Medical Devices.
Retrospective clinical
n=508 images
endpoints: Dice Similarity Coefficient (DSC) >= 0.95
Bench
sample size not stated
endpoints: Angle measurement accuracy (1°); Distance measurement accuracy (1mm); 3D subluxation accuracy (1%)
Bench
sample size not stated
endpoints: Accuracy similar to manual positioning with a 3 mm mean distance
Bench
sample size not stated
endpoints: Frames per second; Jitter; Packet loss
Bench
sample size not stated
endpoints: Measurement precision of one millimeter
Reported performance (1 observation)
source quote (p.11)
“The braln™ system's automatic segmentation was validated against manual segmentation, meeting a mean Dice Similarity Coefficient (DSC) on the testing set greater than or equal to 0.95.”
Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.
Predicate network
Postmarket — what happened after clearance
Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.
Reimbursement — how devices like this got paid
Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).