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The OSSIOfiber® Staple is indicated for fixation of arthrodesis, osteotomies in hand or foot surgery in the presence of appropriate brace and/or immobilization.

The number and size of the OSSIOfiber® Staples must be adapted to the indication.

The OSSIOfiber® Staple is a fixation implant made of degradable poly (L-lactide) (PLDLA) reinforced with continuous mineral fibers. OSSIOfiber® implants have been shown to be biocompatible. The polymer content degrades by hydrolysis into alpha-hydroxy acids that are metabolized by the body. The fibers are made of minerals that are found in natural bone. As the OSSIOfiber® implants degrade, the load transfers to the surrounding anatomy throughout the healing period of the osteotomy, fusion, or fracture. Substantial degradation takes place within approximately 18 months as shown in pre-clinical studies, thus eliminating the requirement for future hardware removal surgery.

The OSSIOfiber® Staples are supplied sterile, for single patient use only, and non-pyrogenic. They are available in several sizes: 11-25 mm bridge lengths, and 15-22 mm leg lengths.

The OSSIOfiber® Staples are designed to be used with commonly available orthopedic surgical tools such as ISO 9714 compatible instrumentations.

This document, K212594, is a 510(k) Premarket Notification for the OSSIOfiber® Staple. It outlines the FDA's determination of substantial equivalence to a legally marketed predicate device. This document focuses on the mechanical and material properties of the absorbable staple, not on an AI/ML-driven device or study involving human readers analyzing images. Therefore, many of the requested points regarding AI/ML study design, reader performance, ground truth establishment for AI training sets, etc., are not applicable to the content of this document.

However, I can extract information related to the device's performance based on non-clinical data and the rationale for its acceptance.

Here's a breakdown of the requested information based on the provided document:

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

The document does not provide a direct table of numerical "acceptance criteria" against "reported performance" for the device in the typical AI/ML context (e.g., sensitivity, specificity thresholds). Instead, it states that the device's performance was evaluated through non-clinical mechanical testing and compared to a predicate device. The acceptance criterion is "at least equivalent performance" to the predicate.

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

This document details non-clinical laboratory testing of mechanical properties, not a clinical study on human subjects or an AI/ML test set. Therefore, concepts like "test set sample size" (in terms of patient data) and "data provenance" (country of origin, retrospective/prospective) are not applicable. The testing was conducted in a laboratory setting. The specific number of samples for each mechanical test (e.g., number of staples tested for bending) is not detailed in this summary, but standards like ASTM F564-17 typically specify minimum sample sizes for such 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 is not applicable as the device is an implant, and the testing involves mechanical and material properties, not interpretation of medical images or data by experts to establish a "ground truth" for an AI model. "Ground truth" here refers to the measured physical properties of the device.

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

Not applicable. This is not an AI/ML study involving human readers and adjudicated interpretations. Mechanical tests have objective outputs (e.g., force, displacement, cycles to failure).

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 document describes a medical device (an absorbable staple), not an AI-driven diagnostic or assistive tool, and therefore 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 algorithm. The "standalone performance" refers to the intrinsic mechanical properties of the device itself.

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

For the mechanical testing, the "ground truth" is defined by the measured physical properties (e.g., ultimate bending strength, fatigue life, pull-out force) determined through standardized laboratory tests (e.g., ASTM F564-17). For biocompatibility, the ground truth is established by compliance with ISO 10993 standards and a rationale.

8. The sample size for the training set

Not applicable. This is not an AI/ML device that requires training data.

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

Not applicable. As above, this is not an AI/ML device.

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