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The Modified Acumen Single-Lumen Delivery Sheath is intended for the introduction of various types of pacing or defibrillator leads and catheters.

The Modified Acumen Single-Lumen Delivery Sheath is designed to aid in the introduction of various types of pacing or defibrillator leads and catheters. The sheath will be packaged with a dilator and is designed to be slittable. The Acumen Single-Lumen Delivery Sheath is available in a range of inner diameters, from 4F to 9F. The Modified Acumen Single-Lumen Delivery Sheath is available in straight and multiple curved configurations.

This document describes a 510(k) premarket notification for a medical device and is not a study that proves the device meets specific acceptance criteria in the way a clinical trial or performance study would. It's a regulatory submission asserting substantial equivalence to existing devices. Therefore, many of the requested data points (like sample size for test sets, number of experts, MRMC studies, standalone algorithm performance, training set details) are not applicable or available within this document.

However, I can extract the relevant information from the provided text based on the 510(k) process.

Here's the breakdown:

1. Table of Acceptance Criteria and Reported Device Performance

As this is a 510(k) submission, "acceptance criteria" and "reported device performance" are primarily framed around substantial equivalence to predicate devices rather than a direct clinical performance study with numerical criteria. The acceptance criteria for the FDA in a 510(k) is whether the new device is "substantially equivalent" to a legally marketed predicate device.

Study Proving Substantial Equivalence:

The "study" in this context is the 510(k) Premarket Notification itself, which argues for substantial equivalence based on a comparison to predicate devices and in-vitro testing.

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

• Sample size for test set: Not applicable (N/A) in the context of a 510(k) summary relying on substantial equivalence and in-vitro testing. The document states "In-vitro testing has been performed," but does not provide the sample size of tested devices.
• Data provenance: N/A. The in-vitro testing was likely conducted by Acumen Medical, Inc. or a contracted lab. The document does not specify country of origin or whether it was retrospective/prospective in a clinical sense.

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

• Number of experts: N/A, as clinical "ground truth" establishment by experts is not described for a test set in this type of submission. The evaluation for substantial equivalence is primarily based on regulatory standards, engineering testing, and comparison to predicate devices by the manufacturer and the FDA.
• Qualifications of experts: N/A.

4. Adjudication method for the test set

• Adjudication method: N/A. This is typically relevant for studies involving human interpretation or clinical outcomes, which are not detailed here.

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 study: No. This device is a physical delivery sheath, not an AI or imaging diagnostic device.
• Effect size: N/A.

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

• Standalone study: No. This is a physical medical device, not a software algorithm.

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

• Type of ground truth: N/A in the clinical sense. For the in-vitro testing, the "ground truth" would be the pre-defined engineering specifications and performance requirements for the device components and full assembly. The primary "ground truth" for the 510(k) submission is the documented characteristics and performance of the predicate devices to which it is being compared.

8. The sample size for the training set

• Sample size for training set: N/A. This term is relevant for machine learning algorithms, not for the development and testing of a physical medical device described here.

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

• Ground truth establishment for training set: N/A.

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The Modified Acumen Single-Lumen Delivery Sheath is intended for the introduction of various types of pacing or defibrillator leads and catheters.

The Modified Acumen Single-Lumen Delivery Sheath is designed to aid in the introduction of various types of pacing or defibrillator leads and catheters. The sheath will be packaged with a dilator and is designed to be slittable. The Modified Acumen Single-Lumen Delivery Sheath is available in a range of inner diameters, from 4F to 6F.

This document describes a 510(k) premarket notification for a medical device called the "Modified Acumen Single-Lumen Delivery Sheath". This type of submission focuses on demonstrating substantial equivalence to a predicate device, rather than providing extensive clinical study data as would be found in a PMA.

Therefore, the requested information regarding acceptance criteria and performance data for an AI-powered device, involving sample sizes, expert ground truth, MRMC studies, and standalone performance, is not applicable to this 510(k) submission.

This submission is for a physical medical device (a catheter introducer sheath), not an AI/software as a medical device (SaMD). The "testing" mentioned refers to in-vitro testing of the physical device's components and does not involve AI performance metrics.

Here's a breakdown of why each requested point is not present or applicable:

1. A table of acceptance criteria and the reported device performance: Not applicable. The document states "In-vitro testing has been performed and all components, subassemblies, and/or full devices met the required specifications for the completed tests." However, it does not provide a table of these specifications or detailed performance metrics. This is typical for a 510(k) of this nature, where adherence to internal design specifications and equivalence to a predicate device are the primary focus.
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. This refers to physical in-vitro tests, not an AI test set.
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, as there is no AI algorithm being evaluated against expert ground truth.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable, as there is no AI algorithm and no expert adjudication process described.
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 a physical medical device, not an AI-assisted diagnostic tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable, this is a physical medical device, not an AI algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable, as there is no AI algorithm that requires ground truth for its evaluation as per the provided document. The "ground truth" for the device's function would be its ability to physically introduce leads and catheters, assessed through engineering and bench testing.
8. The sample size for the training set: Not applicable, as this is not an AI/machine learning device.
9. How the ground truth for the training set was established: Not applicable, as this is not an AI/machine learning device.

Summary from the provided documents:

• Device Type: Catheter Introducer Sheath (physical medical device)
• Purpose of Submission: Demonstrate substantial equivalence to a predicate device (Acumen Single-Lumen Delivery Sheath K053400).
• Primary Evidence: In-vitro testing (details not provided beyond "met the required specifications") and comparison of materials, intended use, method of operation, and construction to the predicate device.
• Conclusion: The FDA determined the device is substantially equivalent to legally marketed predicate devices.

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The Acumen Single-Lumen Delivery Sheath is indicated for the introduction of various types of pacing or defibrillator leads and catheters.

The Acumen Single-Lumen Delivery Sheath is designed to aid in the introduction of various types of pacing or defibrillator leads and catheters. The sheath will be packaged with a dilator and is designed to be slittable.

This document describes a 510(k) premarket notification for a medical device called the "Acumen Single-Lumen Delivery Sheath". This type of submission is a declaration that a new device is "substantially equivalent" to one or more legally marketed predicate devices, rather than a study proving performance against acceptance criteria in the way one might for a novel AI device.

Therefore, many of the requested categories related to AI device studies (like sample size for test/training sets, expert ground truth adjudication, MRMC studies, or standalone performance of an algorithm) are not applicable to this submission.

Here's a breakdown of the information provided in the context of your request:

1. Table of Acceptance Criteria and Reported Device Performance

Study Proving Device Meets Acceptance Criteria:

The study proving the device meets the acceptance criteria is not a clinical trial or AI performance study, but rather a premarket submission demonstrating substantial equivalence to existing devices through a combination of:

• In-vitro testing: Performed to ensure the device components, subassemblies, and the full device meet required specifications.
• Animal testing: Also performed to ensure the device components, subassemblies, and the full device meet required specifications.
• Material compatibility assessment: Confirming that all materials are suitable for medical use and have a history of use in previously cleared products.
• Comparison to Predicate Devices: A detailed comparison of the new device's intended use, method of operation, construction, and materials to those of the predicate devices.

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

• Not Applicable. This is not an AI device study requiring a traditional "test set" from a dataset of images or clinical cases. The testing involved in-vitro and animal studies, which are typically defined by the specific test protocols and models used, rather than a "sample size" of patient data.
• Data Provenance: The document does not specify the country of origin for the in-vitro or animal testing. It also doesn't explicitly state if the testing was retrospective or prospective, though in-vitro and animal studies for device clearance are typically prospective.

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 device study involving expert human review for ground truth establishment. The "ground truth" here is compliance with engineering specifications and functional performance in in-vitro and animal models.

4. Adjudication method for the test set

• Not Applicable. No expert adjudication method (like 2+1, 3+1) is mentioned or relevant for this type of device 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

• Not Applicable. This is not an AI device. No MRMC study was conducted.

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

• Not Applicable. This is not an AI device. No standalone algorithm performance was assessed.

7. The type of ground truth used

• For the in-vitro and animal testing: The ground truth would be engineering specifications, functional performance metrics, and safety outcomes as defined by the test protocols.
• For the substantial equivalence claim: The "ground truth" is the characteristics and performance of the legally marketed predicate devices.

8. The sample size for the training set

• Not Applicable. This is not an AI device, so there is no training set mentioned or relevant.

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

• Not Applicable. As no training set exists for an AI model, this question is not relevant.

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