K-numberK251096
Device namePeekMed web
ApplicantPeek Health, S.A.
Product codeQIH
Device classClass II
Decision dateJul 14, 2025
DecisionSubstantially Equivalent
Regulation892.2050
AI Summary extracted from FDA summary PDF · never regenerated
Intended use

PeekMed web is a cloud-based medical software system designed to help surgeons perform pre-operative planning for orthopedic procedures such as hip, knee, upper limb, and foot surgeries. The system imports patient medical imaging studies (X-rays, CT scans, and MRI), displays them in 2D or 3D environments, performs measurements and overlays digital representations of surgical implants, and generates final planning reports. It is intended for use by qualified healthcare professionals in planning surgical procedures on adult patients with diagnosed injuries or disabilities.

Technological characteristics

The subject device is substantially identical to its predicate (K250042) in software architecture, workflow, and core functionality. Both are cloud-based systems requiring internet connection, supporting automatic or manual pre-surgical planning with 2D/3D visualization, model positioning, dimensioning, and digital overlap of prosthetic templates. The key technical difference is that the subject device includes a new machine learning model variant for knee segmentation that additionally supports MRI imaging, whereas the predicate supported only X-ray and CT scans for knee segmentation.

Test standards cited

Not stated in this summary.

Substantial equivalence argument

The device is substantially equivalent because it shares identical indications for use, intended user population (adult patients, healthcare professionals), anatomical regions (hip, knee, upper limb, foot), contraindications (none), and core functional features with its predicate. Both devices perform the same workflows, have the same software architecture, and generate comparable planning outputs. The addition of MRI support for knee segmentation via a new ML model variant does not constitute an intended-use change because the development, verification, validation, and deployment processes are identical to the predicate, and all ML models met predefined acceptance criteria (segmentation DICE ≥90%, landmarking MRE ≤7mm, classification accuracy ≥90%, detection MAP ≥90%) through independent external validation on diverse datasets.

Extracted by AI from the official FDA summary PDF →
Source

View the full FDA submission: accessdata.fda.gov

Researching this as a predicate?
Want a transparent AI-ranking score, AI-discovered related predicates, ongoing safety and warning-letter monitoring, full predicate chain lineage, and a drafted SE rationale — all saved to your own project? That's what an account adds.
Start free trial →

Everything you need for a 510(k) submission. Nothing you don't.

14-day free trial. No setup. Cancel anytime.

Start free trial →
Building an AI or ML-enabled device? Predicate search, PCCP tracking, and AI-specific FDA intelligence — built exclusively for AI/ML devices. Try AIFDA Intel →