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Exactech Novation Crown Cup TM Constrained Liners and Rings are components of the Exactech Novation® non-cemented acetabular cup system. The device is intended for use in primary or revision patients at high risk of hip dislocation due to a history of prior dislocation, bone loss, soft tissue laxity, neuromuscular disease, or intra-operative instability and for whom all other options to constrained acetabular components have been considered. They are intended for press-fit fixation and are compatible with Exactech press-fit or cemented femoral stem components.

The proposed Novation® Constrained Acetabular Liners are modifications of the previously cleared AcuMatch A-Series predicates. The design features of the subject devices are summarized below:

NOVATION CONSTRAINED LINERS

• Sphere and taper inner diameter geometry for compatibility with the Novation . Crown Cup
• Increased range-of-motion and lever-out moment by decreasing the height of the . constraining petals, increasing the constraining diameter and increasing the lead in chamfer
• Addition of a 36mm ID option. .

NOVATION CONSTRAINED RINGS

• The constraining ring will be made of Ti-6Al-4V per ASTM F1472 instead of Ti- 6Al-4V per ASTM F136.
• Ring snaps into a locking feature on the Novation Crown Cup instead of press-. fit.
• Inner and outer diameter of the constraining ring modified for compatibility with . the proposed constrained liner and Novation Crown Cup.

The provided text describes a 510(k) submission for the Exactech® Novation Crown Cup™ Constrained Liners and Rings. This is a medical device submission, and as such, the "acceptance criteria" and "study" are not in the context of typical AI/ML performance metrics, but rather engineering evaluations and substantial equivalence to a predicate device.

Here's an interpretation based on the given information:

1. Table of Acceptance Criteria and Reported Device Performance

For medical devices like this, the "acceptance criteria" for a 510(k) submission are typically related to a demonstration of substantial equivalence to an already legally marketed (predicate) device, and the "performance" is verified through engineering evaluations to ensure it's "adequate for anticipated in vivo use."

Study Proving Device Meets Acceptance Criteria:

The study that proves the device meets the acceptance criteria is referred to as "Engineering evaluations."

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

• Sample Size for Test Set: Not specified in the provided text. For mechanical testing of medical devices, sample sizes are typically determined by statistical methods for specific tests (e.g., fatigue, wear, pull-out strength). The summary only states "Engineering evaluations were conducted."
• Data Provenance: Not specified, but generally, such engineering evaluations are conducted in a laboratory setting by the manufacturer (Exactech, Inc.) based in the USA (Gainesville, Florida). The data would be prospective, as it's generated specifically for this submission.

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

This concept (experts establishing ground truth) is primarily applicable to studies involving human interpretation (e.g., radiology for AI models). For a mechanical device 510(k) submission, "ground truth" is typically objective engineering measurements and established material science standards.

• No specific number of human "experts" establishing ground truth for the test set (engineering evaluations) is mentioned. The "experts" in this context would be the engineers and scientists conducting and reviewing the tests, following established ASTM/ISO standards for medical device testing. Their qualifications would be in biomedical engineering, materials science, mechanical engineering, etc., likely with experience in orthopedic implants.

4. Adjudication Method for the Test Set

Again, "adjudication" usually refers to resolving disagreements between human readers/interpreters. For engineering evaluations, the "adjudication" is inherent in the scientific and engineering review process, ensuring test protocols are followed, data is correctly analyzed, and conclusions drawn are valid.

• No explicit "adjudication method" like 2+1 or 3+1 is mentioned or applicable in the context of engineering evaluations for a mechanical device.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

• No, an MRMC comparative effectiveness study was not done. This type of study is relevant for AI-powered diagnostic tools where human readers are involved. The Novation Crown Cup™ is a mechanical orthopedic implant, not an AI diagnostic device.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

• No, a standalone (algorithm only) study was not done. This concept applies to AI/ML software. The Novation Crown Cup™ is a physical medical device.

7. Type of Ground Truth Used

• For the engineering evaluations, the "ground truth" would be objective physical measurements and adherence to established engineering standards (e.g., ASTM F1472 for material, other ASTM/ISO standards for mechanical properties like fatigue, wear, range-of-motion, lever-out moment testing). The "truth" is whether the device meets predefined mechanical performance thresholds or demonstrates equivalent performance to the predicate device under specific test conditions.

8. Sample Size for the Training Set

• This concept is not directly applicable. In the context of mechanical device design, there isn't a "training set" in the machine learning sense. Instead, design iterations and simulations might be performed, but these are not referred to as training data for an algorithm. The 510(k) process focuses on validating the final product, not on the data used to "train" its design.

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

• Not applicable, as there is no "training set" in the AI/ML sense for this mechanical device. The design evolution and verification are based on engineering principles, materials science, clinical experience with previous devices, and established standards, rather than machine learning "ground truth."

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