K Number
K193552
Device Name
InnovaMatrix
Date Cleared
2020-10-21

(306 days)

Product Code
Regulation Number
N/A
Panel
SU
Reference & Predicate Devices
AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
Intended Use

InnovaMatrix™ is indicated for the management of wounds including; partial- and full-thickness wounds, pressure ulcers, venous ulcers, diabetic ulcers, chronic vascular ulcers, tunneled/undermined wounds (donor sites/grafts, post-Mohs surgery, post-laser surgery, podiatric, wound dehiscence), trauma wounds (abrasions, seconddegree burns and skin tears) and draining wounds.

The device is intended for one-time use.

Device Description

InnovaMatrix™ is a decellularized extracellular matrix (ECM) topical wound covering derived from porcine placental tissue. Triad processes the tissue into the ECM topical wound covering.

InnovaMatrix™ is composed of collagen, elastin, laminin, fibronectin, hyaluronic acid and sulfated glycosaminoglycans.

The wound dressing is provided in sheets that are approximately 40-100 microns thick in sizes ranging from 1 x 1cm to 5 x 5cm. They are provided as single-use, sterile wound coverings.

AI/ML Overview

The provided text is a 510(k) summary for the InnovaMatrix™ device. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving effectiveness through clinical trials with specific acceptance criteria as would be required for a novel device or a PMA.

Therefore, the document does not contain the information requested regarding acceptance criteria and a study proving the device meets them in the way a clinical trial for a new diagnostic algorithm or treatment efficacy would. Specifically, it does not include:

  • A table of acceptance criteria and reported device performance.
  • Sample sizes used for a "test set" in the context of an AI/diagnostic algorithm study.
  • Information about expert ground truth establishment (number of experts, qualifications, adjudication method).
  • Details of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study or human reader improvement with AI assistance.
  • Standalone performance data for an algorithm.
  • Specific "ground truth" types (pathology, outcomes data) for clinical efficacy.
  • Training set sample size or how ground truth was established for a training set.

Instead, the study presented in this document is a substantial equivalence demonstration based on:

  1. Biocompatibility Testing: Evaluating the safety profile of InnovaMatrix™ with various in vitro and in vivo tests (cytotoxicity, skin sensitization, intracutaneous reactivity, acute/subacute/subchronic systemic toxicity, implantation, genotoxicity, material-mediated pyrogenicity). The conclusion drawn is that the biocompatibility profile is comparable to the predicate device.
  2. Laboratory Testing: Analysis of the physical and chemical properties of the device (cell debris, collagen/elastin/etc. analysis, endotoxin, residual moisture, water absorption, tensile strength, viral inactivation, shelf life, heavy metals). These tests aim to characterize the material and ensure it meets relevant standards.
  3. Clinical Testing (Human Repeat Insult Patch Testing and Skin Prick Testing): These are safety tests to assess allergic/irritant potential, not efficacy studies.
    • Human Repeat Insult Patch Testing: 58 subjects completed, no reactions.
    • Skin Prick Testing: 23 subjects completed, 22 showed no reactions, 1 had a low-grade positive reaction that resolved.

Key takeaway: The provided information is about demonstrating the safety and similar technological characteristics of a wound dressing to an existing device, not about proving performance against specific acceptance criteria in a study format typically associated with AI or diagnostic device performance evaluation.

If this were a study proving the device meets acceptance criteria for a diagnostic AI, the structure and content would be vastly different, focusing on metrics like sensitivity, specificity, AUC, human-AI collaboration impact, etc.

N/A