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Cordis Angiographic Catheters with Marker Bands are designed to provide angiographic visualization and linear measurement of the vasculature when combined with the delivery of radiopaque contrast media to selected sites in the vascular system.

The SUPER TORQUE® MB Angiographic Catheter is a thin plastic tube which is inserted into an artery through a small incision in the skin. The catheter is guided to the area being examined, and contrast material is injected through the tube and images are captured using a small dose of ionizing radiation (x-rays). Each SUPER TORQUE® MB Angiographic Catheter consists of a braided Polyurethane body and a non-braided Polyurethane tip section having multiple, radiopaque marker bands.

This document is a 510(k) Premarket Notification for a medical device called the SUPER TORQUE® MB Angiographic Catheter. It is not an AI/ML device, and therefore the concepts of acceptance criteria and a study to prove the device meets those criteria (in the context of AI/ML performance metrics) do not apply in the traditional sense.

The document describes a submission seeking substantial equivalence to a legally marketed predicate device (K992347). For such devices, the "acceptance criteria" are generally that the new device is as safe and effective as the predicate device. This is typically demonstrated through a comparison of technological characteristics, and often, non-clinical (bench) testing. Clinical studies are not always required if substantial equivalence can be shown otherwise.

Here's why the requested information cannot be provided in the context of AI/ML device evaluation:

• No AI/ML Component: The SUPER TORQUE® MB Angiographic Catheter is a physical medical device (a thin plastic tube for angiography), not a software algorithm or an AI/ML system.
• No "Performance Metrics" in the AI Sense: The performance of this device is assessed by its physical characteristics, material properties, and functionality (e.g., ability to deliver contrast, radiopacity of marker bands), not by metrics like sensitivity, specificity, AUC, or reader performance improvement often used for AI.
• No "Training/Test Sets" for AI: The concept of training sets and test sets with ground truth established by experts is irrelevant for a physical catheter.

However, I can extract and present the available information in a slightly adapted format, focusing on how substantial equivalence was demonstrated, which is the closest analog to "meeting acceptance criteria" for this type of device.

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

For this physical angiographic catheter, "acceptance criteria" relate to demonstrating substantial equivalence to a predicate device. The performance is assessed through bench testing and comparison of fundamental technological characteristics.

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

Not applicable in the context of AI/ML. For a physical device, testing is typically done on manufactured samples. The document states "No new testing was required to be conducted due to addition of contraindication in the IFU." It also mentions "minor manufacturing updates were made, additional biocompatibility testing was performed, and additional sterilization facilities were qualified." This implies some level of physical testing (bench testing) on device samples, but specific sample sizes for these tests are not detailed in this submission. Data provenance would be from laboratory test reports.

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

Not applicable. This is not an AI/ML device requiring expert adjudication for ground truth.

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

Not applicable. This is not an AI/ML device requiring expert 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

Not applicable. This is not an AI/ML device. No MRMC study was performed. The document explicitly states: "No clinical data was required in support of the proposed change to the predicate device cleared under K992347."

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

Not applicable. This is not an AI/ML device, so there is no algorithm to evaluate in standalone mode.

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

Not applicable. For a physical device, "ground truth" would be engineering specifications and validated test methods for material properties, dimensions, and mechanical performance.

8. The sample size for the training set

Not applicable. This is not an AI/ML device.

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

Not applicable. This is not an AI/ML device.

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The Cordis TrapEase™ Permanent Vena Cava Filter is indicated for the prevention of recurrent pulmonary embolism via percutaneous placement in the inferior vena cava in the following situations: pulmonary thromboembolism when anticoagulants are contraindicated, failure of anticoagulant therapy in thromboembolic diseases, emergency treatment following massive pulmonary embolism where anticipated benefits of conventional therapy are reduced and chronic, recurrent pulmonary embolism where anticoagulant therapy has failed or is contraindicated.

The VisEase Angiographic Vessel Dilator is designed to provide angiographic visualization and linear measurement of the vasculature when combined with the delivery of radiopaque contrast media to the vena cava.

The subject TrapEase Permanent Vena Cava Filter with the VisEase Angiographic Vessel Dilator most notably differs from the predicate TrapEase Permanent Vena Cava Filter and Introduction Kit with regards to the vessel dilator that is included as a component in the Introduction Kit. In addition to its original function as a vessel dilator, which was featured with the predicate Cordis TrapEase Introduction Kits, the subject VisEase Angiographic Vessel Dilator features new intended uses involving angiographic visualization and linear measurement. These new intended uses of the subject VisEase Angiographic Vessel Dilator are addressed in updated product labeling herein and are identical to the intended uses of the predicate Cordis Super Torque MB Angiographic Catheter (reference K992347, determined substantially equivalent on October 8, 1999). Besides the aforementioned changes in intended use for the VisEase Angiographic Vessel Dilator, the TrapEase Permanent Vena Cava Filter and all other components used with the subject device remain unchanged from #K000062, #K003964 and #K010083).

The provided document is a 510(k) premarket notification for the "Cordis TRAPEASE™ Permanent Vena Cava Filter with the VISEASE™ Angiographic Vessel Dilator." This type of submission generally focuses on demonstrating substantial equivalence to a predicate device rather than presenting a standalone study with acceptance criteria and device performance metrics in the way a clinical trial or AI device might.

Therefore, many of the requested elements (e.g., sample size for test set, number of experts, adjudication method, MRMC study, sample size for training set, ground truth for training set) are not typically found in this type of regulatory submission because they are not required to demonstrate substantial equivalence for this conventional medical device.

However, I can extract the information that is present and indicate what is not applicable based on the document.

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

For this device, "acceptance criteria" are primarily related to conforming to established standards and demonstrating functional performance through non-clinical testing. The document states that the safety and effectiveness were demonstrated through "non-clinical design verification tests and analyses."

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

This information is not provided in the document as it's a 510(k) summary relying on non-clinical engineering and bench testing, not human subject data for "test sets" in the typical sense. Data provenance is not specified for the non-clinical tests.

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 information is not applicable for this type of submission, as ground truth is not established by human experts for non-clinical bench testing.

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

This information is not applicable for this type of 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

An MRMC study is not applicable to this device, as it is a physical medical device (vena cava filter and dilator), not an AI diagnostic tool or system that assists human readers.

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

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

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

Given that the testing consisted of "non-clinical design verification tests and analyses" (Visual and Dimensional Inspection, Catheter Sheath Introducer Compatibility, Flow Rate Testing, Hydrodynamic Testing, Pull Strength Testing), the "ground truth" would be established by engineering specifications, material science standards, and performance metrics defined for the device during its design and development, rather than medical ground truths like pathology or expert consensus.

8. The sample size for the training set

This information is not applicable as this is a physical medical device and not an AI algorithm requiring a training set.

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

This information is not applicable as this is a physical medical device and not an AI algorithm.

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