brAIn™ Shoulder Positioning

K252665

Avatar Medical · cleared 2025-10-20 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.7)
“The brAIn™ Shoulder Positioning software is a cloud-based application intended for shoulder surgeons.”
Algorithmautomatic segmentation using machine learning
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.”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
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)

Standalone

n=508 images

endpoints: Dice Similarity Coefficient (DSC) >= 0.95

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.

Standalone

sample size not stated

endpoints: compliance with required performance standards

Standalone

sample size not stated

endpoints: 1° for angle measurement; 1 mm for distance measurement; 1% for 3D subluxation

Standalone

sample size not stated

endpoints: accuracy similar to manual positioning with a 3 mm mean distance

Bench

sample size not stated

endpoints: frames per seconds; jitter; packet loss

Bench

sample size not stated

endpoints: precision of one millimeter

Reported performance (6 observations)

diceas written: “Dice Similarity Coefficient (DSC)”0.95
source quote (p.11)
“The brAIn™ 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.”
accuracyas written: “Angle Measurement Accuracy”1
source quote (p.12)
“The acceptance criterion for the Measurement Accuracy Performance Validation Protocol is set at 1° for angle measurement”
accuracyas written: “Distance Measurement Accuracy”1
source quote (p.12)
“1 mm for distance measurement”
accuracyas written: “3D Subluxation Accuracy”1
source quote (p.12)
“and 1% for 3D subluxation when updating the position of landmarks and implants.”
accuracyas written: “Landmark Positioning Accuracy”3
source quote (p.12)
“Validation compared pre-positioning with final positions adjusted by experts, achieving accuracy similar to manual positioning with a 3 mm mean distance as the acceptance criterion.”
ppvas written: “Ruler Tool Precision”1
source quote (p.12)
“The tool is expected to display the linear (Euclidean) distance between two user-selected points on the scapula's unreamed 3D mesh, with a precision of one millimeter.”

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

0
recalls in product code, 24mo
3
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
0
drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device); a recall is shown as device-attributed only when the recall record itself lists this clearance number. 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).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Radiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K252665