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.
AlgorithmArtificial intelligence / machine learning model that reconstructs a 3D model of the torso from video and predicts Cobb angle.
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. Predicted cobb angle is derived from our machine learning model.
Adaptive (vs locked)No
source quote (p.11)
For machine learning purposes the training, validation and test data is strictly separated. For training, training and validation data is used, but separated by labeling. The training process has no access to test data. Test data is only accessed and tested on when training has completed.
PCCPFDA source did not state this
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)

Retrospective clinical

n=212 patients

endpoints: Prediction of Cobb angle within 10 degrees of X-ray

Bench

sample size not stated

endpoints: Software unit testing pass with 87% code coverage; Software system-level testing pass; Software integration testing pass; Fulfillment of design requirements and established performance criteria

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)

Bench

n=30 other

endpoints: Safe-use test pass rate for lay users; Safe-use test pass rate for healthcare professionals; Ability to perform critical task of capturing video; User face intuitiveness rating; Ease of understanding instructions rating; Instructions of Use clarity rating

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). 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).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K232023