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The Günther Tulip Filter implant is intended for the prevention of recurrent pulmonary embolism (PE) via placement in the vena cava in the following situations:

• · Pulmonary thromboembolism when anticoagulant therapy is contraindicated;
• · Failure of anticoagulant therapv in thromboembolic diseases:
• · Emergency treatment following massive PE where anticipated benefits of conventional therapy are reduced; and
• · Chronic, recurrent PE where anticoagulant therapy has failed or is contraindicated.

The Günther Tulip Filter implant may be retrieved if clinically indicated; please refer to the "Optional Filter Retrieval" section of the Instructions for Use for more information.

The product is intended for percutaneous placement via a femoral or jugular vein for filtration of inferior vena cava (IVC) blood to prevent PE.

The Celect Platinum Filter implant is intended for the prevention of recurrent pulmonary embolism (PE) via placement in the vena cava in the following situations:

• · Pulmonary thromboembolism when anticoaqulant therapy is contraindicated;
• · Failure of anticoagulant therapy in thromboembolic diseases;
• · Emergency treatment following massive PE where anticipated benefits of conventional therapy are reduced: and
• · Chronic, recurrent PE where anticoaqulant therapy has failed or is contraindicated.

The Celect Platinum Filter implant mav be retrieved if clinically indicated: please refer to the "Optional Filter Retrieval" section of the Instructions for Use for more information.

The product is intended for percutaneous placement via a femoral or juqular vein for filtration of inferior vena cava (IVC) blood to prevent PE.

The Celect Platinum Filter Set consists of a filter composed of a paramagnetic cobalt chromium alloy (49 mm long when compressed to a diameter of 30 mm) with platinum markers, preloaded on a femoral filter introducer with a flexible tip; a 7 French coaxial introducer system (compatible with a 0.035 inch wire guide); and a 10 French pre-dilator with hydrophilic coating for vessel access. The introducer dilator has eight sideports and two radiopaque markers 30 mm apart (end-to-end). The Celect Platinum Filter implant is designed to act as a permanent filter or retrievable filter.

The Günther Tulip Filter Set consists of a filter composed of a paramagnetic cobalt chromium alloy (50 mm long when compressed to a diameter of 30 mm), preloaded on a femoral filter introducer; a 7 French coaxial introducer system (compatible with a 0.035 inch wire guide); and a 10 French pre-dilator with hydrophilic coating for vessel access. The introducer dilator has eight sideports and two radiopaque markers 30 mm apart (end-to-end). The Günther Tulip Filter implant is designed to act as a permanent filter or retrievable filter.

The provided text describes a 510(k) premarket notification for vena cava filters, specifically the Celect Platinum® and Günther Tulip® filter sets. The submission focuses on demonstrating substantial equivalence to previously cleared predicate devices, primarily due to updates in the device labeling to include clinical evidence and updated MRI safety information.

Therefore, the acceptance criteria and study proving the device meets the acceptance criteria are centered around demonstrating that the modified device (with updated labeling) is still substantially equivalent to the predicate device, and that the clinical evidence supports the updated labeling regarding the safety and effectiveness of the existing device. The document does not describe an AI/ML-based device or a study involving human readers or ground truth establishment in the typical sense of evaluating an algorithm's performance. Instead, it refers to clinical studies that already took place to gather data for the updated labeling of the physical medical device.

Given this context, I will address the questions as they relate to the information provided about the device's substantial equivalence and the underlying clinical studies. Many of the questions, particularly those related to "AI/ML acceptance criteria," "sample sizes for test and training sets," "expert adjudication," "MRMC studies," and "ground truth establishment" for AI, are not directly applicable to a 510(k) for a physical medical device. I will indicate where the information is not present or not relevant to the type of device and submission described.

Here's an interpretation based on the provided document:

Acceptance Criteria and Device Performance (in the context of Substantial Equivalence and Labeling Updates for a Physical Device)

The overall acceptance criterion for this 510(k) submission is Substantial Equivalence to the predicate devices (Cook Celect Platinum Vena Cava Filter Sets K211875 and Günther Tulip Vena Cava Filter Sets K211874). This means demonstrating that the modified devices (with updated labeling) are as safe and effective as the predicate devices. The performance testing detailed here supports this claim.

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

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

The document states that the clinical data to support the updated labeling originated from two studies:

• Cook IVC Filter Study
• PRESERVE Study

These were:

• Sample Size: Not explicitly stated for either study in the provided text. The term "subjects" is used, implying a cohort of patients.
• Data Provenance:
  • Country of Origin: Not explicitly stated, but indicated as "multi-center" studies, suggesting various locations.
  • Retrospective or Prospective: Both studies were conducted as multi-center, prospective, Investigational Device Exemption (IDE) studies.

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 question is not applicable in the context of this 510(k) submission, as it relates to evaluating a physical medical device (vena cava filter) rather than an AI/ML algorithm that requires expert annotation for ground truth. The clinical studies evaluated the performance of the physical filters directly in patients. Decisions on safety and effectiveness (e.g., presence of PE, DVT, complications) would have been made by the medical professionals involved in the care of the patients in the studies.

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

This question is not applicable, as there is no "test set" in the sense of image data requiring expert adjudication for AI/ML algorithm evaluation. Clinical outcomes in the described IDE studies would have been assessed by the treating physicians and study investigators, likely with predefined diagnostic criteria and endpoints.

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 such MRMC study was conducted or described in the provided text. This type of study is relevant for evaluating the impact of AI assistance on human performance (e.g., radiologists interpreting images), which is not the subject of this 510(k) for a physical medical device.

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

Not applicable. This submission is not for an AI/ML algorithm; it is for physical medical devices (vena cava filters).

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

The "ground truth" for evaluating the performance of the vena cava filters in the two IDE studies (Cook IVC Filter Study and PRESERVE Study) was clinical outcomes data gathered prospectively from patients. This would include:

• Safety Endpoints: Rates of filter complications (e.g., filter embolization, clinically significant perforation, new DVT, caval thrombotic occlusion, SAEs).
• Effectiveness Endpoints: Prevention of pulmonary embolism (PE).
• Retrieval Measures: Data related to filter retrieval if applicable.

These outcomes would have been based on clinical assessments, imaging, and lab results collected during the studies.

8. The sample size for the training set

Not applicable. This submission is for a physical medical device. Training sets are relevant for machine learning models, which are not described here.

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

Not applicable. As there is no AI/ML training set, there is no ground truth establishment process for it.

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