Momentum Spine

K232023

Momentum Health Inc. · cleared 2023-10-04 · product code LDK · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“Momentum Spine is an optical contour sensing mobile application intended to quantify asymmetries, assess body angles and curve progression related to postural asymmetries, including scoliosis. The subject Momentum Spine device does not have a hardware component, it only utilizes mobile phone cameras to capture a video of the patient's torso to generate a 3D view of surface topography.”
Algorithmartificial intelligence; machine learning model
source quote (p.6)
“From a simple video taken on a mobile device, Momentum Spine (‘app') reconstructs a 3D model of the torso to quantify asymmetry using 3D imaging and artificial intelligence. The subject device provides the following quantitative anatomical measurements: • Al predicted Cobb Angle (degrees); Predicted cobb angle is derived from our machine learning model.”
Adaptive (vs locked)FDA source did not state this
PCCPNo
Cybersecurity addressedYes
source quote (p.10)
“The cybersecurity considerations of data confidentiality, data integrity, data availability, denial of service attacks, and malware were adequately addressed utilizing platform controls, application controls, and procedure controls, and evidence was provided for the intended performance of the controls.”

Validation studies (3)

Bench

sample size not stated

endpoints: Safety and performance of the Momentum Spine has been evaluated and verified against software specifications and applicable performance standards through software verification and validation testing.

standards: IEC 62304 Edition 1.1 2015-06 CONSOLIDATED VERSION Medical device software - Software life cycle processes [FR Recognition Number 13-79], FDA Guidance: Content of Premarket Submissions for Device Software Functions (June 2023), FDA Guidance: Content of Premarket Submissions for Management of Cybersecurity in Medical Devices (October 2014)

Retrospective clinical

n=212 patients

endpoints: The prediction should be within 10 degrees of the X-ray, taken on the same day.

Bench

n=15 other

endpoints: The purpose of the usability validation test plan was to demonstrate that the intended video capture workflow and training methodology can successfully be used by the intended users to capture 2 videos for 3D analysis.

Reported performance (0 observations)

FDA source did not state a quantitative performance metric — non-reporting is itself the signal.

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
0
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/K232023