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The Exsomed Wrist Plating System is intended for the repair of fractures and fusions of the hand and wrist.

The Exsomed Wrist Plating System is a set of implantable metallic (Titanium Alloy) bone plates, locking and non-locking screws of various lengths. The system consists of a series of straight tubular plates of varying lengths as well as a 4-hole carpal fusion plate. Screws are provided for plate fixation in locking and non-locking versions and in Ø2.5 and Ø3.2mm diameters. All of the Implants are made from Titanium Alloy (Ti-6AL-4VELI) per ASTM F136 and are provided non-sterile.

This looks like a 510(k) premarket notification for a medical device, which typically does not involve the kind of AI/machine learning performance study you're asking about. The document describes a biocompatibility and mechanical performance assessment for a traditional medical device (metallic bone fixation plates and screws), not an AI/ML powered device.

Therefore, most of the requested information regarding acceptance criteria, sample sizes for test/training sets, expert ground truth, MRMC studies, standalone performance, and ground truth establishment cannot be found in this document because it pertains to a different type of device and regulatory submission.

However, I can extract the relevant "performance analysis" information related to the device's mechanical properties, which is the "acceptance criteria" and "study" described in this context.

Here's a breakdown of what can be extracted based on the provided text, and what is not applicable in this context:

Acceptance Criteria and Study for Exsomed Wrist Plating System (Mechanical Performance)

This document describes the premarket notification (510(k)) for the Exsomed Wrist Plating System, a traditional medical device consisting of metallic bone plates and screws for hand and wrist fracture repair and fusion. The "acceptance criteria" and "study" described herein relate to the mechanical and material performance of these physical implants, not to the performance of an AI/ML algorithm.

1. Table of Acceptance Criteria and Reported Device Performance

Information Not Applicable/Available for an AI/ML Device (Based on this document):

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. The "tests" were mechanical assays on physical samples of the device and predicate devices, not data analysis for an AI/ML algorithm.
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 for mechanical testing is based on industry standards (ASTM) and physical measurements, not expert consensus on medical images/data.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. No expert adjudication is involved in 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: Not applicable. This device is not an AI/ML diagnostic or assistive tool for human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): The "ground truth" for this device's performance is derived from standardized mechanical testing methods (e.g., ASTM F382-17) and material specifications, which provide objective, measurable results.
8. The sample size for the training set: Not applicable. There is no training set for a mechanical device.
9. How the ground truth for the training set was established: Not applicable. There is no training set for a mechanical device.

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