OptimMRI (v2)

K242054

Rebrain, SAS · cleared 2024-08-12 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.3)
OptimMRI is a software application intended to aid qualified medical professionals in processing, visualizing, and interpreting anatomical structures from medical images.
Algorithmadvanced image processing techniques and machine learning models
source quote (p.4)
OptimMRI (v2) is used as an aid to localize regions of the brain such as Subthalamic Nuclei (STN) and Ventral Intermediate Nucleus (VIM) using advanced image processing techniques and machine learning models trained on a proprietary clinical database.
Adaptive (vs locked)No
source quote (p.4)
OptimMRI (v2) is used as an aid to localize regions of the brain such as Subthalamic Nuclei (STN) and Ventral Intermediate Nucleus (VIM) using advanced image processing techniques and machine learning models trained on a proprietary clinical database.
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.5)
ANSI UL 2900-1 First Edition 2017, Standard for Safety, Standard for Software Cybersecurity Network-Connectable Products, Part 1: General Requirements.

Validation studies (1)

Bench

sample size not stated

endpoints: STN and VIM region of interest (ROI) annotation accuracy; surface distances of inferior-lateral regions of VIM structure

standards: IEC 62304 Edition 1.1 2015-06 CONSOLIDATED VERSION, Medical device software - Software life cycle processes, ISO 14971 Third Edition 2019-12, Medical devices - Application of Risk Management to medical devices, IEC 62366-1 Edition 1.1 2020-06 CONSOLIDATED VERSION, Medical devices - Part 1: Application of usability engineering to medical devices, AAMI CR34971:2022, Guidance on the Application of ISO 14971 to Artificial Intelligence and Machine Learning, IEC/TR 80002-1 Edition 1.0 2009-09, Medical device software - Part 1: Guidance of the application of ISO 14971 to medical device, ISO 20417 First edition 2021-04 Corrected version 2021-12, Medical devices - Information to be supplied by the manufacturer, ISO 15223-1 Fourth edition 2021-07, Medical devices - Symbols to be used with information to be supplied by the manufacturer - Part 1: General requirements, ANSI UL 2900-1 First Edition 2017, Standard for Safety, Standard for Software Cybersecurity Network-Connectable Products, Part 1: General Requirements., IEC 80001-1 Edition 1.0 2010-10 Application of risk management for IT-networks incorporating medical devices - Part 1: Roles, responsibilities and activities., AAMI TIR57:2016, Principles for medical device security - Risk management, ANSI AAMI SW96:2023, Standard for medical device security - Security risk management for device manufacturers

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
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). 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/K242054