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Fixation of tendons and ligaments during orthopedic reconstruction procedures such as Anterior Cruciate Ligament (ACL) Reconstruction.

The Femoral Hook device includes a body and an arm. The body incorporates an eyelet that provides a means to attach the soft tissue grafts. The arm is used to anchor the body to the cortical bone.

The provided document is a 510(k) summary for the Femoral Hook device, which is a soft tissue anchor. It primarily focuses on demonstrating substantial equivalence to a predicate device based on mechanical testing and does not involve an AI/ML component or related studies that would typically require the requested information on acceptance criteria, reader studies, or ground truth establishment relevant to AI performance.

Therefore, most of the requested information cannot be extracted from this document as it pertains to AI/ML device evaluation, which is not applicable here.

Here's what can be extracted and what cannot:

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

Explanation: The document states "Mechanical testing indicated that the Femoral Hook had a greater pull-out strength than the predicate device." However, no specific quantitative acceptance criteria (e.g., "pull-out strength must be X Newtons") or detailed performance measurements (e.g., "Femoral Hook achieved Y Newtons vs. Predicate Device's Z Newtons") are provided in this summary. The primary criterion for substantial equivalence in this context is that the new device performs at least as well as, or better than, the predicate device in relevant mechanical tests.

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 for the test set: Not specified in the document. Mechanical testing details are summarized, not fully disclosed.
• Data provenance: Not applicable. This is mechanical testing of a medical device, not a study involving patient data.

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 pertains to AI/ML device evaluation against clinical assessments. This document describes mechanical testing of a physical device.

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

• Not Applicable. Adjudication methods like 2+1 or 3+1 refer to expert consensus processes for establishing ground truth in clinical data reviews (e.g., radiology images). This document describes mechanical testing.

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. The document explicitly states: "No clinical testing was provided as a basis for substantial equivalence." A MRMC study is a type of clinical study, often involving human readers and AI.
• Effect size: Not applicable as no such study was conducted.

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 algorithm or AI model.

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

• Not Applicable/Intrinsic Property: The "ground truth" for mechanical testing would be the actual physical properties and performance measured under controlled laboratory conditions (e.g., actual pull-out strength, breaking stress). It doesn't rely on expert consensus or pathology in the same way an AI diagnostic device would.

8. The sample size for the training set

• Not Applicable. This is not an AI/ML device; therefore, there is no training set in the context of machine learning.

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

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

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