Search Results

The Breast-Med Tissue Marker is indicated for use to radiographically mark soft tissue during a surgical procedure or for future surgical procedures.

The Breast-Med Tissue Marker is a sterile, nonpyrogenic, single use tissue marker consisting of a polymeric tube filled with a dessicated solution of sodium chloride with trace amounts of gadolinium chelate that is visible on standard radiographs (x-ray, mammography) as well as ultrasound and Magnetic Resonance Imaging (MRI). The tissue marker is placed into soft tissue during open, percutaneous, or endoscopic procedures to radiographically mark a surgical location.

The provided text describes a 510(k) premarket notification for the Breast-Med Tissue Marker. This is a medical device, and the document focuses on demonstrating its substantial equivalence to previously approved predicate devices. It does not present a study with detailed acceptance criteria and performance data in the typical sense of an AI/algorithm-based diagnostic device.

Instead, the "performance testing" described refers to bench testing to verify physical properties and radiographic visibility, rather than a clinical study evaluating diagnostic accuracy against a ground truth.

Therefore, I cannot provide a table of acceptance criteria and reported device performance from this document in the way you might expect for an AI system. However, I can extract information related to the device's characteristics and the type of performance testing mentioned.

Here's a breakdown of the available information based on your request:

1. Table of Acceptance Criteria and Reported Device Performance:

The document summarizes performance testing but does not provide specific acceptance criteria or quantitative performance metrics in a table. It only states that bench testing was performed to "verify that the subject device has equivalent radiographic visualization to the predicates." The actual results of this verification are not detailed.

2. Sample Size Used for the Test Set and Data Provenance:

The document refers to "bench testing" using "tissue and phantom models." It does not specify a sample size for these models or their provenance (country of origin, retrospective/prospective). This type of testing typically uses a controlled set of physical models rather than clinical patient data.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications:

This information is not applicable or provided. Bench testing of physical markers for radiographic visibility would not typically involve human experts establishing a diagnostic "ground truth" in the way an AI diagnostic system would. The "ground truth" here would be the physical presence and visibility of the marker itself, assessed objectively.

4. Adjudication Method for the Test Set:

This information is not applicable or provided. As there's no diagnostic "ground truth" established by experts to be adjudicated, there's no adjudication method mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

No, an MRMC comparative effectiveness study was not done. This document describes a physical medical device (a tissue marker), not an AI algorithm designed to interpret medical images. Therefore, the concept of human readers improving with AI assistance is not relevant to this submission.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done:

No, a standalone algorithm-only performance assessment was not done. This is a physical tissue marker, not an algorithm.

7. The Type of Ground Truth Used:

The "ground truth" in this context is the physical presence and visibility of the tissue marker in various imaging modalities, assessed objectively through bench testing. It's not a clinical ground truth like pathology, expert consensus, or outcomes data, as this device itself is a marker, not a diagnostic tool that interprets data.

8. The Sample Size for the Training Set:

Not applicable. There is no "training set" for this type of physical medical device. This is not an AI/machine learning device.

9. How the Ground Truth for the Training Set Was Established:

Not applicable. There is no training set or associated ground truth for this physical device.

Ask a specific question about this device