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The FibermarX™ Radiopaque Tissue Marker is intended to be implanted into the body to accurately visualize and constitute the reference frame for stereotactic radiosurgery and radiotherapy target localization. In addition, the markers are indicated in situations where tissue needs to be marked for future medical procedures such as IMRT/IGRT.

The device is a sterile, single-patient-use, barium sulfate infused non-absorbable polymer monofilament that is visible on standard radiographs (x-ray, CT, mammography). FibermarX™ is passed through soft tissue and tied into place during open, percutaneous, or arthroscopic/laparoscopic/endoscopic procedures and standard surgeon's knots or a continuous running outline are used to quickly mark the soft tissue for subsequent imaging or for radiotherapy target localization.

The provided document is a 510(k) premarket notification for a medical device called the FibermarX™ Radiopaque Tissue Marker. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than providing extensive clinical study data that would typically include detailed acceptance criteria and performance metrics of the new device in a standalone clinical trial.

Therefore, the document does not contain the requested information regarding acceptance criteria and a study that proves the device meets those criteria in the context of a comparative effectiveness study, standalone AI algorithm performance, or using experts to establish ground truth. The submission focuses on non-clinical testing to demonstrate substantial equivalence to a predicate device.

Here's a breakdown of what can be extracted or inferred from the document based on the standard 510(k) submission process:

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

The document does not provide a formal table of acceptance criteria and reported device performance in the manner one would see for a clinical trial or AI algorithm validation. Instead, it describes non-clinical tests conducted to support substantial equivalence. The "performance" is generally demonstrated by showing that the device is comparable to the predicate device in specific aspects.

Inferred "Acceptance Criteria" (based on non-clinical tests) and "Reported Device Performance":

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

• Sample Size for Test Set: Not applicable in the context of human data. The "test set" here refers to physical samples of the device used for non-clinical testing (e.g., individual markers for biocompatibility, imaging, and radiation impact). The specific number of physical units tested is not detailed, but it would have been sufficient for the conducted non-clinical tests.
• Data Provenance: Not applicable as this is non-clinical testing of the device itself, not human data. The tests were performed by the manufacturer or a contracted lab.

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)

• Number of Experts: Not applicable. Ground truth, in the context of clinical or AI studies, is not established for non-clinical device testing. The results of the physical tests are the "ground truth" for the device's characteristics.

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

• Adjudication Method: Not applicable. This concept applies to expert review of clinical data, which is not part of this 510(k) non-clinical submission.

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

• MRMC Comparative Effectiveness Study: No, an MRMC comparative effectiveness study was not conducted and is not mentioned. This type of study would be relevant for evaluating diagnostic imaging devices or AI-assisted systems reading images from human patients, which is not the nature of this device or its submission.

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

• Standalone Performance: No, a standalone algorithm performance study was not done. This device is a physical tissue marker, not an algorithm.

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

• Type of Ground Truth: For the non-clinical tests, the "ground truth" is derived from objective, quantitative measurements and laboratory analyses (e.g., chemical analysis results for extractables, physical measurements for mechanical properties after radiation, optical/imaging assessments of visibility).

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This device is not an AI algorithm, so there is no training set in the conventional sense.

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

• Ground Truth for Training Set Establishment: Not applicable, as there is no training set for this device.

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

The device is a sterile, single-patient-use, barium sulfate infused polypropylene suture that is radiopaque using standard radiographs (x-ray, mammography). The device is placed into soft tissue sites during open, percutaneous, or endoscopic procedures and standard surgeon's knots are tied to quickly and inexpensively mark the soft tissue so that the integrity and location of the marked soft tissue can be evaluated.

This document is a 510(k) summary for a medical device called a "Radiopaque Tissue Marker." It outlines the device's characteristics, its intended use, and the non-clinical testing performed to establish substantial equivalence to a predicate device.

Here's an analysis based on the provided text, addressing your questions:

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

The document does not explicitly state "acceptance criteria" in a quantitative format with corresponding reported performance metrics for the device. Instead, it describes non-clinical tests performed to demonstrate substantial equivalence to a predicate device. The performance is assessed against the predicate device, not against specific, pre-defined numerical thresholds for accuracy, sensitivity, or specificity.

However, based on the non-clinical testing mentioned, we can infer performance aspects:

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

The document describes non-clinical laboratory testing. It does not mention a "test set" in the context of patient data, nor does it specify any sample sizes for patients or data provenance (country of origin, retrospective/prospective). The testing involved physical devices and materials, not patient data in the way one would analyze for an AI algorithm.

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)

This is not applicable to the study described. The study involves non-clinical testing of a physical device. There is no "ground truth" establishment in the context of expert consensus on medical images or diagnoses.

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

This is not applicable for the study described. There is no "test set" of patient data requiring adjudication of ground truth by multiple experts.

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

No MRMC study was performed. The device is a physical radiopaque tissue marker, not an AI algorithm. Therefore, an "effect size of human readers improve with AI vs without AI assistance" is not relevant.

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

No standalone algorithm performance study was done. The device is a physical radiopaque tissue marker, not an AI algorithm.

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

This is not applicable for the study described. The "ground truth" for non-clinical device testing would be the physical and chemical properties of the materials and their behavior under controlled laboratory conditions, as measured by standard testing methods (e.g., ISO 10993 for biocompatibility, specific imaging protocols for radiopacity). It is not based on expert consensus, pathology, or outcomes data related to patient diagnosis.

8. The sample size for the training set

This is not applicable. The device is a physical medical device, not an AI algorithm that requires a "training set."

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

This is not applicable. There is no AI algorithm or training set associated with this device.

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