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The TUMARK® Flex is intended for radiographically and radiologically percutaneous marking of soft tissue, especially breast tissue, via a clip marker.

The TUMARK® Flex is not indicated to be used with magnetic resonance imaging (MRI) techniques.

The TUMARK® Flex is a sterile, single use, preloaded tissue site marking system consisting of a non-absorbable Nitinol clip-marker, a guide wire or tube and a handle with ejection mechanism. The guide wire is composed of a flexible tube, a distal ramp made of surgical high-grade steel with an opening for releasing the clip marker and a depth stopper with snap-in tip. The guide tube is composed of a tube section, a distal ramp made of surgical steel with an opening for releasing the clip marker, and a marking line, which shows the orientation of the ejection port for the clip marker. The handle is provided with a slider by means of which the clip can be released. The clip marker is situated in the distal ramp. TUMARK® Flex can be used together with, e.g. ultrasound and stereotactic X-ray imaging procedures.

The TUMARK® Flex is not indicated to be used in Magnetic Resonance Tomography (MRT). However, the clip marker placed in the patient can be exposed to a magnetic field of up to 3.0 Tesla, for instance in follow-up examinations.

The provided document is a 510(k) summary for the SOMATEX® TUMARK® Flex Tissue Site Marking System. It primarily focuses on demonstrating substantial equivalence to predicate devices and does not contain detailed information about acceptance criteria or a specific study proving device performance against those criteria. The submission is not for a software or AI-driven device, but a physical medical device. Therefore, many of the requested items (e.g., sample size for test set, number of experts, adjudication method, AI assistance studies, training set details) are not applicable to this type of submission.

Here's a summary of what can be extracted based on the provided text, and where information is not available:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state quantitative acceptance criteria or report device performance against such criteria. The submission focuses on demonstrating substantial equivalence to legally marketed predicate devices, implying that the new device performs acceptably because it is "identical or similar in technology, design and material," with "minor technological differences" that "raise no new questions of safety and effectiveness."

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Not applicable. This is a submission for a physical medical device. The document does not describe a "test set" in the context of data used for algorithm validation or a clinical study with a specified sample size for proving performance against acceptance criteria.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. This is a submission for a physical medical device, not a diagnostic or interpretive algorithm requiring expert ground truth.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable. This is a submission for a physical medical device, not a diagnostic or interpretive algorithm requiring adjudicated ground truth.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This device is a tissue site marker, not an AI-assisted diagnostic tool.

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

Not applicable. This is a physical medical device, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Not applicable. The concept of "ground truth" as typically applied to diagnostic AI/software is not relevant to the approval of this physical medical device. Device performance is generally demonstrated through engineering testing, biocompatibility testing, material safety, and substantial equivalence to existing devices.

8. The sample size for the training set

Not applicable. This is a physical medical device, not a machine learning model requiring a training set.

9. How the ground truth for the training set was established

Not applicable. This is a physical medical device, not a machine learning model.

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