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The Hoffmann® II MRI components are external fixation frame components for use with the components of the Hoffmann® External Fixation System, Hoffmann® II External Fixation System, Monotube® TRIAX™ External Fixation System and in conjunction with Apex® Pins. It is intended to provide stabilization of open and/or unstable fractures and where soft tissue injury precludes the use of other fracture treatments such as IM rodding or casting.

This Special 510(k) submission is intended to add additional components to the predicate Hoffmann® II MRI External Fixation System.

This document is a 510(k) premarket notification for Hoffmann® II MRI External Fixation System components. It primarily focuses on demonstrating substantial equivalence to a predicate device and does not contain information about acceptance criteria or a study proving device performance against such criteria in the way a clinical study for an AI/ML device would.

Here's an analysis based on the provided text, highlighting the absence of the requested information due to the nature of this submission:

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

No acceptance criteria or device performance metrics (e.g., sensitivity, specificity, AUC) are presented in the document. This is a 510(k) for external fixation components, not an AI/ML device, so these types of performance metrics are not applicable in this context. The closest equivalent is stating that "Mechanical testing demonstrated comparable mechanical properties to the predicate components and testing in a Magnetic Resonance Environment established that the components could be safely used in Magnetic Resonance Imaging under predetermined conditions." However, specific criteria for "comparable" or "safely used" are not detailed.

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

Not applicable. There is no test set in the context of an AI/ML device's performance evaluation. The "testing" mentioned is likely mechanical and MRI compatibility testing of the physical device components, not a dataset of patient images.

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. There is no "ground truth" in the AI/ML sense, as this is a physical medical device.

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

Not applicable. No test set or adjudication method is 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 not an AI/ML device, so no MRMC study or assessment of human reader improvement with AI assistance would be relevant.

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

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

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

Not applicable. There is no ground truth, as this is a physical medical device. The "truth" in this context would be governed by engineering standards for mechanical strength and MRI safety.

8. The sample size for the training set

Not applicable. There is no "training set" for physical medical device components.

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

Not applicable. There is no training set or ground truth in the context of this device.

Summary based on the provided document:

The document describes a 510(k) submission for new components to an existing external fixation system. The primary "study" mentioned is mechanical testing and MRI compatibility testing. These tests were conducted to demonstrate that the new components possess "comparable mechanical properties to the predicate components" and can be "safely used in Magnetic Resonance Imaging under predetermined conditions." The document does not provide specific acceptance criteria or detailed results of these tests, which is typical for a 510(k) summary relying on substantial equivalence to a predicate device. This type of submission does not involve clinical studies with human "readers" or "ground truth" as would be required for AI/ML-driven devices.

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The Hoffmann® II MRI components are external fixation frame components for use with the components of the Hoffmann® External Fixation System, Hoffmann® II External Fixation System, Monotube® TRIAX" External Fixation System and in conjunction with Apex® Pins. It is intended to provide stabilization of open and/or unstable fractures and where soft tissue injury precludes the use of other fracture treatments such as IM rodding or casting.

This Special 510(k) submission is intended to address modifications to the predicate Hoffmann This openal J ro(is) bachines to include components, which may be used safely for Magnetic Resonance Imaging (MRI).

This document describes a Special 510(k) submission for the Hoffmann® II MRI External Fixation System. The submission addresses modifications to existing Hoffmann external fixation devices to include components that can be safely used in a Magnetic Resonance Imaging (MRI) environment.

Here's an analysis of the provided information regarding acceptance criteria and the study that proves the device meets those criteria:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify the sample size used for the test set for either the mechanical testing or the MRI environment testing. It also does not provide information about data provenance (e.g., country of origin, retrospective or prospective).

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Experts

This document does not provide information about the number of experts, their qualifications, or how ground truth was established for the test data. This type of information would typically be relevant for studies involving diagnostic interpretation (e.g., image analysis by radiologists). For a device like an external fixation system, "ground truth" would relate to objective measurements of mechanical properties and MRI safety parameters, rather than expert interpretation.

4. Adjudication Method for the Test Set

This document does not describe any adjudication method. As mentioned above, since this involves objective mechanical and MRI safety testing, expert adjudication in the traditional sense is not applicable.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted or described. This type of study is relevant for AI-powered diagnostic tools to assess how AI assistance impacts human reader performance. The Hoffmann® II MRI External Fixation System is a medical device, not an AI diagnostic tool.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study Was Done

No, a standalone study in the context of an "algorithm only" or "AI" performance was not done. The device is a physical medical device, not a software algorithm. The "testing in a Magnetic Resonance Environment" would be considered a standalone test of the device's physical properties under MRI conditions.

7. The Type of Ground Truth Used

For the mechanical properties, the ground truth would be established through objective measurements obtained from standardized mechanical testing protocols (e.g., tensile strength, fatigue testing, torsional stiffness). The "ground truth" for MRI safety would be established by objective measurements and observations according to recognized consensus standards for MRI compatibility (e.g., assessment of B1+ transmit field mapping, specific absorption rate (SAR), heating, artifact generation, force, torque, and image quality degradation). These are not typically "expert consensus" in the way a diagnostic image is, but rather adherence to established engineering and safety standards.

8. The Sample Size for the Training Set

This document does not refer to a "training set." This term is specific to machine learning and AI algorithms. The device undergoes engineering design, prototyping, and rigorous physical testing, not a "training" phase in the AI sense.

9. How the Ground Truth for the Training Set Was Established

Since there is no "training set" as understood in machine learning/AI, this question is not applicable. The design and validation of this physical device rely on engineering principles, material science, and adherence to relevant national and international standards for medical device safety and performance.

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