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The Endomag Magseed Magnetic Marker is intended to be placed percutaneously in the breast to mark temporarily (

The Magseed Magnetic Marker is identical to the Sentimark Magentic marker described in K153044. Only the trade name has changed. The Magseed Magnetic Marker is supplied to end users as a pre-packaged sterile, single-use delivery system. The system comprises an individual soft magnetic marker (magnetic seed) and a 18-gauge needle delivery system that is used to deliver the seed to the intended deployment location. This product is designed as a single use device that is supplied sterile in an individual sealed tyvek pouch.

This Premarket Submission is solely concerned with a reduction in the length of the 18-gauge needle cannula that is used to introduce the Magseed Magnetic Marker. K153044 describes two device configurations, where:

• REF: SM18-1-12 has a length of 12cm
• REF: SM18-1-20 has a length of 20cm

This submission solely considers a reduction in the needle canulla to a minimum length of 5cm.

• REF: SM18-1-05 has a length of 5cm .
• REF: SM18-1-07 has a length of 7cm .

There is no revision to the implanted marker.

The Sentimag Detector remains identical to that described in K153044

The Sentimag System is intended for the non-imaging detection and localization of the Magseed Magnetic Marker that has been implanted in a lumpectomy site intended for surgical removal.

The Sentimag System and Magseed Magnetic Marker System are designed for use in an operating room environment by suitably trained physicians who are experienced in diagnosis and treatment of breast lesions.

The Sentimag System aids the surgeon to detect surgically invasively magnetic marker material that has been placed for the purpose of detecting a non-palpable lesion, and to locate target excision sites.

Prior to a lumpectomy procedure, the Magseed Magnetic Marker is placed percutaneously into the breast, using imaging quidance such as ultrasound or radiography, to temporarily mark a site intended for surgical removal. During a surqical procedure, the hand-held Sentimag probe emits an alternating maqnetic field that detects the magnetic response of the Magseed magnetic marker, this signal is converted by the base unit into a visual and audible response that is similar in use to the predicate device.

This document is a 510(k) premarket notification for a medical device called the "Magseed Magnetic Marker System." It is primarily focused on demonstrating substantial equivalence to a previously cleared predicate device (K153044), particularly regarding a reduction in the length of the needle cannula used for delivery.

Based on the provided text, the document does not describe a study involving acceptance criteria and device performance in the context of human reader performance or algorithm-only performance for AI/ML devices. The submission is for a physical medical device (a tissue marker and its detection system), not an AI/ML algorithm.

Therefore, the requested information categories (#1-9) which are typical for AI/ML device studies, are largely not applicable to this document. The document describes a traditional medical device submission where equivalence is established through demonstrating that changes (like needle length) do not introduce new questions of safety or effectiveness, often relying on non-clinical testing and comparison to a predicate.

Here's the breakdown of why each point is not directly addressed by the provided text:

1. A table of acceptance criteria and the reported device performance: Not present. This document is not evaluating the performance of an AI/ML model against specific metrics like sensitivity, specificity, or AUC.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. There is no "test set" in the context of an AI/ML evaluation.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. No ground truth establishment for a diagnostic AI/ML algorithm is described.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
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 does not involve human readers interpreting images with or without AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm-only device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable in the context of AI/ML. The "ground truth" for this device would be its physical properties, sterility, safe implantation, and accurate detection, which are assessed through engineering tests and clinical use, not a labeled dataset.
8. The sample size for the training set: Not applicable. No training set for an AI/ML model is mentioned.
9. How the ground truth for the training set was established: Not applicable.

Summary based on the provided document:

The document describes a 510(k) submission for the Magseed Magnetic Marker System, which is a physical device used to mark lesions in the breast for surgical removal. The current submission is primarily to gain clearance for reduced needle cannula lengths (5cm, 7cm) compared to previously cleared lengths (12cm, 20cm).

The core of the submission relies on demonstrating that these shorter needles do not introduce new questions of safety or effectiveness because:

• The implanted marker ("Magseed Magnetic Marker") remains identical to the predicate device component.
• The detection system ("Sentimag System") remains identical to the predicate device.
• There are no revisions in device materials or processes for the common design elements.
• The product risk management file was reviewed, and no additional risks were identified for the shorter cannulas.

Therefore, the "study" proving the device meets acceptance criteria is implicitly the demonstration of substantial equivalence through technical comparisons and a risk assessment, rather than a clinical trial or AI/ML performance study as typically understood in the context of acceptance criteria tables.

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