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REVERSE PROSTHESIS (METAL-BACK OR POROUS GLENOID IMPLANT) The ARROW Reverse Shoulder Prosthesis is indicated for patients with severe shoulder arthropathy and a grossly deficient rotator cuff or a previously failed shoulder joint replacement with a grossly deficient rotator cuff. A functional deltoid muscle and adequate glenoid bone stock are necessary to use this device. The humeral stem is intended for cementless application while the glenoid baseplate (metal-back or porous) is intended for cementless application with the addition of bone screws for fixation.

The ARROW® Reverse porous glenoid base is used in total reverse shoulder prosthesis and is designed to articulate with the ARROW® Reverse shoulder system (cleared in K112193). The porous glenoid base is used with bone screws for fixation (cleared in K112193). The ARROW® Reverse porous glenoid is intended to be implanted using the dedicated instrumentation supplied by the manufacturer. This instrument set is common for all the configurations of prosthesis: simple humeral prosthesis, total anatomical prosthesis and reverse prosthesis.

This document is a 510(k) Premarket Notification from the FDA regarding the ARROW® Reverse Porous Glenoid. It is a submission for a Class II medical device (shoulder joint prosthesis) and primarily focuses on demonstrating substantial equivalence to existing predicate devices.

This document describes a medical device (shoulder prosthesis), NOT an AI/ML powered device.

Therefore, most of the requested information about acceptance criteria, study details, expert involvement, and ground truth establishment, which are typical for AI/ML device evaluations, are not applicable here.

Here's a breakdown based on the provided document:

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

Since this is a submission for a physical medical device (shoulder prosthesis) and not an AI/ML powered device, the "acceptance criteria" are related to mechanical and material performance, and "device performance" refers to the results of engineering tests.

2. Sample size used for the test set and the data provenance:

• Sample Size: The document does not specify the exact sample sizes (e.g., number of prostheses tested) for the ASTM standards or LAL testing. These are typically engineering tests performed on a defined number of device units or material samples per the standard's requirements.
• Data Provenance: The tests were conducted by the manufacturer, Fournitures Hospitalières Industrie, based in France. The data would be "prospective" in the sense that the tests were performed specifically for this 510(k) submission on newly manufactured devices or samples.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not Applicable. This is a physical device submission. "Ground truth" in the context of AI/ML models (e.g., expert annotation of medical images) is not relevant here. Device performance is assessed through standardized mechanical and biological tests. The "experts" would be the engineers and technicians performing and interpreting these standardized tests.

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

• Not Applicable. As above, adjudication methods common in AI/ML performance evaluation (e.g., for reconciling expert disagreements) are not pertinent to the mechanical testing of a medical implant.

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-assisted device, so MRMC studies and human reader improvement with AI assistance are irrelevant to this submission.

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.

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

• Not Applicable for AI/ML "ground truth". For a physical device, "ground truth" relates to established engineering principles and the specifications within the ASTM standards. The device is expected to perform in accordance with the established mechanical and material properties defined by these standards.

8. The sample size for the training set:

• Not Applicable. There is no "training set" as this is not an AI/ML device.

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

• Not Applicable. There is no "training set" or "ground truth" establishment in the AI/ML sense.

In summary, this document is a regulatory submission for a traditional implantable medical device, demonstrating its substantial equivalence to previously cleared devices through adherence to established material and mechanical performance standards, rather than through AI/machine learning performance metrics.

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BePOD® EZ Weil screws are designed for the following indication:
-Treatment of metatarsalgia on the lateral metatarsal shafts (side toes), by Weil osteosynthesis, following osteotomies carried out:

• by the opened surgery (standard Weil) technique,
• by the DMMO percutaneous technique (Distal Metatarsal Mini Invasive Osteotomy).

The BePOD® EZ Weil Screws are threaded titanium screws which are self-drilling and selftapping designed for use in the foot. Once completely implanted, the screw is designed so the surgeon can snap off the introducer at the snap off axis.
The BePOD® EZ Weil Screws is intended to be implanted using the dedicated instrumentation supplied by the manufacturer.

This document is a 510(k) premarket notification for a medical device called "BePOD® EZ Weil Screws." It describes the device's intended use and compares it to predicate devices to establish substantial equivalence for regulatory approval.

Based on the provided text, the device is a bone fixation fastener (specifically, screws for foot surgery). The document does not describe an AI/ML-based medical device or a study involving acceptance criteria for AI/ML performance, human readers, ground truth establishment, or training/test sets in the context of diagnostic or prognostic performance.

Therefore, I cannot fulfill the request to describe the acceptance criteria and the study that proves the device meets the acceptance criteria in the context of AI/ML device performance, as the document does not contain this information.

The document focuses on:

• Device Type: Metallic bone fixation screws.
• Indications for Use: Treatment of metatarsalgia on lateral metatarsal shafts by Weil osteosynthesis (open surgery or percutaneous technique).
• Substantial Equivalence: Comparison to legally marketed predicate devices based on materials, size ranges, and design features.
• Non-Clinical Testing: Mentions "Engineering Analysis" and "LAL pyrogen testing" but these are mechanical and biocompatibility tests, not performance studies as requested for AI/ML.

The "study" referenced in the document is a comparison for substantial equivalence, not a performance study as would be conducted for an AI/ML diagnostic or prognostic tool.

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Arthrodesis cannulated screws are designed for the following indication: Arthrodesis of the metatarsal-phalangeal joint of the big toe.

BePOD® Cannulated Arthrodesis Screws includes the following elements: BePOD® Arthrodesis cannulated screws. The BePOD® Cannulated Arthrodesis Screws is intended to be implanted using the dedicated instrumentation supplied by the manufacturer.

This is a 510(k) premarket notification for a medical device, specifically the BePOD® Cannulated Arthrodesis Screws. Such notifications typically focus on demonstrating substantial equivalence to a predicate device rather than providing detailed studies on a device's performance against specific acceptance criteria for AI algorithms.

Therefore, the requested information regarding acceptance criteria, study details for AI performance, sample sizes for test/training sets, expert qualifications, ground truth establishment, MRMC studies, and standalone performance is not applicable to this document.

This document describes a traditional medical device (screws for bone fixation), not an AI/ML-driven device. The "Performance" section refers to material testing (LAL testing) and compliance with manufacturing regulations, not performance metrics based on data analysis or AI algorithms.

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