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The FOOTPRINT® MINI PK, 3.5mm Suture Anchor is only intended for the reattachment of soft tissue to bone for the following indications:

Foot and Ankle:

• · Medial or lateral instability repairs/reconstructions
• · Achilles tendon repairs/reconstructions
  Knee:
• · Extra-capsular repairs
• -Medial collateral ligament
• -Lateral collateral ligament
• -Posterior oblique ligament

The Smith & Nephew FOOTPRINT? MINI PK, 3.5mm Suture Anchor is a fixation device intended to provide reattachment of soft tissue to bone. The device consists of a two-piece suture anchor comprised of an anchor body and inner plug, preassembled onto an insertion device. The device is sold with a disposable suture threader.

This response pertains to a medical device submission, specifically a 510(k) for the "FOOTPRINT® MINI PK, 3.5mm Suture Anchor". The provided text describes a physical medical device and not a software algorithm or AI-powered device. Therefore, many of the requested categories related to AI/software performance evaluation (like test set data provenance, expert ground truth, MRMC studies, training set details, etc.) are not applicable to this type of submission.

The provided document details the regulatory clearance of a physical medical device, not a diagnostic AI or imaging analysis software. Thus, the information requested about acceptance criteria for AI algorithms, training sets, ground truth establishment methods typical for AI, multi-reader multi-case studies, and human-in-the-loop performance is not found in the provided text as it pertains to a different type of medical product.

However, I can extract information regarding the device's performance testing and acceptance, which are analogous to "acceptance criteria" for a physical device.

Here's the relevant information based on the provided text:

Acceptance Criteria and Device Performance (for a physical medical device)

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

Note: The document states, "Non-clinical bench testing was completed on the subject device, and the device met all required specifications for each test. ... Results for all tests passed." It implies that specific predefined criteria for each test type were met, but the precise numerical or qualitative thresholds for these criteria are not detailed in this publicly available summary.

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: The document does not specify the exact sample size (number of devices/implants) used for each non-clinical bench test.
• Data Provenance: Not applicable as this is bench testing of physical devices, not clinical data or imaging. The tests were performed in a lab setting, likely by Smith & Nephew.

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: "Ground truth" in the context of expert review for medical imaging or AI models is not relevant here. The "ground truth" for the performance of a physical medical device is established through its physical and mechanical properties as measured in bench testing against defined engineering specifications.

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

• Not Applicable: Adjudication methods like 2+1 or 3+1 are used for human review of medical images or data, typically in the context of clinical studies or establishing ground truth for AI. For physical device bench testing, engineering standards and protocols dictate the evaluation, not human adjudication in this sense.

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: MRMC studies are specific to the evaluation of diagnostic imaging devices, often involving human readers and potentially AI assistance. This submission is for a physical orthopedic implant.

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

• Not Applicable: This question refers to the performance of an independent AI algorithm. The device is a physical suture anchor, not an algorithm.

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

• Engineering Specifications/Standards: The "ground truth" for this device's performance is based on established engineering specifications and industry standards for mechanical strength, insertion force, and durability under various loading conditions relevant to its intended use in soft tissue to bone reattachment. The document states, "the device met all required specifications for each test."

8. The sample size for the training set

• Not Applicable: There is no "training set" in the context of a physical device. This term is used for machine learning algorithms.

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

• Not Applicable: As there is no training set for a physical device, this question is not relevant.

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The HEALICOIL◊ PK Suture Anchor with Needles, ULTRATAPE® (Blue) and HEALICOIL◊ PK Suture Anchor with Needles, ULTRATAPE (Blue Cobraid) are intended for use only for the reattachment of soft tissue to bone for the following indications:

Foot & Ankle

• Medial or lateral instability repairs/reconstructions
• Achilles tendon repairs/reconstructions

The Smith & Nephew HEALICOIL® PK Suture Anchor with Needles, ULTRATAPE® (Blue) and HEALICOIL® PK Suture Anchor with Needles, ULTRATAPE® (Blue Cobraid) is a fixation device intended to provide reattachment of soft tissue to bone. The device consists of a suture anchor with a preloaded ULTRATAPE suture with needles assembled on an insertion device.

This document is a 510(k) Premarket Notification from the FDA regarding a medical device, specifically a suture anchor. The information provided heavily focuses on regulatory compliance, device description, intended use, and comparison to predicate devices, rather than detailed performance study data against acceptance criteria typically seen for AI/ML-driven devices.

Therefore, many of the requested details about acceptance criteria, study design for proving device performance, sample sizes, expert involvement, and ground truth establishment (especially in the context of an AI/ML algorithm) are not present in this document. This is because the device described is a physical medical device (suture anchor), not an AI/ML diagnostic or predictive tool.

I will address the elements based on the information that can be inferred for a physical device, and clearly state when information is not available or not applicable given the nature of the device.

Device: HEALICOIL® PK Suture Anchor with Needles, ULTRATAPE® (Blue); HEALICOIL® PK Suture Anchor with Needles, ULTRATAPE® (Blue Cobraid)
Device Type: Soft Tissue Fixation Device (Suture Anchor)

Overall Conclusion from Document: The device is deemed substantially equivalent to a legally marketed predicate device (HEALICOIL PK Suture Anchor, K152566). This determination is based on similarities in indications for use, design features, operational principles, material biocompatibility, and composition. The changes (new inserter and addition of needles to the preloaded suture) are considered minor and do not raise new questions of safety or effectiveness.

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

Note: The document provides a high-level summary that "all components met the required specifications for the completed tests" and that "The results for all testing were passing." Specific quantitative acceptance criteria values (e.g., minimum load force, cycle count) and reported numerical performance metrics are not provided in this summary.

2. Sample size used for the test set and the data provenance

• Sample Size for Test Set: Not specified in the document. The document states "Mechanical testing performed on the device and all components" but does not give sample sizes (e.g., number of anchors tested for each type of mechanical test).
• Data Provenance: Not specified. For mechanical testing of a manufactured physical device, the data would typically originate from in-vitro lab testing, not patient data. The document does not mention the country of origin of the data or whether it was retrospective or prospective, as these concepts are generally not applicable to the type of testing described for this device.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• This question is not applicable to this type of device. The "ground truth" for a physical medical device like a suture anchor is established through engineering specifications, material science standards, and biomechanical testing standards, not expert consensus on diagnostic images or patient outcomes in the way an AI/ML device would require. Performance is measured against physical test standards (e.g., ASTM, ISO standards for implantable devices), not judgments made by experts in a conciliation process.

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

• This question is not applicable. Adjudication methods like 2+1 or 3+1 are used in studies involving human readers or evaluators to resolve discrepancies in interpretation (e.g., in radiology image reading). For the mechanical testing of a suture anchor, performance is objectively measured against pre-defined engineering and material specifications. There is no subjective interpretation requiring adjudication.

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

• This question is not applicable. This device is a physical medical implant (suture anchor), not an AI-assisted diagnostic or treatment planning tool. Therefore, MRMC studies and the concept of human readers improving with AI assistance do not apply here.

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

• This question is not applicable. This device is a physical medical implant, not an algorithm or software.

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

• The "ground truth" for this device's performance is based on predefined engineering specifications, material properties, and biomechanical testing standards (e.g., tensile strength, fatigue life, pull-out strength in a synthetic bone model). This is not derived from expert consensus, pathology, or outcomes data in the usual sense. The document states "all components met the required specifications," implying these engineering-defined benchmarks served as the ground truth.

8. The sample size for the training set

• This question is not applicable to this device. A "training set" refers to data used to train an AI/ML algorithm. This device is a manufactured physical product. Its design and validation rely on engineering principles, material science, and mechanical testing, not a data-driven training process in the AI/ML context.

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

• This question is not applicable, as there is no "training set" for a physical medical device.

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