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Straumann® CARES® Screw-retained Bridges and Bars are indicated for use as bars and bridges that attach to implants to provide support for prosthetic reconstructions such as bridges and overdentures. The final processed products have the purpose of restoring chewing function. Straumann® CARES® Screw-retained Bridges and Bars are indicated for screw-retained restorations. Straumann® CARES® Screw-retained Bridges and Bars are designed to interface with the Bone Level (BL), Tissue Level (TL), and BLX implants of the Straumann Dental Implant System (SDIS).

The Straumann® CARES Screw-Retained Bridges and Bars ("SRBB") are used for the restoration of Straumann dental implants with different endosteal diameters, lengths and platforms (Figure 1). The purpose of this premarket notification is to expand the currently cleared abutment-to-implant interfaces to include the BLX implant system of the Straumann Dental Implant System (SDIS). The materials available include coron and titanium.

SRBB devices facilitate customization to meet the functional and esthetic requirements of the individual patient. They are patient-specific medical devices, i.e., they are designed by a dental professional (clinician or dental technician) and fabricated by Straumann specifically for an individual patient.

SRBB devices are designed via Computer Aided Design (CAD). After importing a scan of the patient model, Straumann® CARES® Visual software includes the ability to generate digital restoration models incorporating the subject devices as well as the predicate devices. The digital restoration model is transferred to the milling center where the restoration is produced using Computer Aided Manufacturing (CAM)techniques.

The provided document is a 510(k) Premarket Notification for the Straumann® CARES® Screw-Retained Bridges and Bars. The purpose of the submission is to expand the cleared abutment-to-implant interfaces to include the BLX implant system. This document does not describe an AI/ML-enabled device or a study involving human readers or expert consensus for ground truth. Instead, it describes a dental device and its performance is evaluated through bench studies, software validation, sterilization validation, and biocompatibility testing.

Therefore, many of the specific details requested in your prompt (e.g., acceptance criteria for AI performance, sample size for test set with data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone AI performance, training set details) are not applicable to this document as it pertains to a physical dental device, not an AI/ML system.

However, I can extract information related to the performance data and the conclusion about substantial equivalence.

Here's what can be extracted and inferred based on the document:

1. Acceptance Criteria and Reported Device Performance:

The document doesn't provide specific numerical acceptance criteria in a table format for this specific 510(k) submission's tests. Instead, it states that the device's substantial equivalence is addressed via bench studies that conform to FDA guidance and ISO standards. The "performance" is implicitly deemed acceptable if it meets these standards and supports the claim of substantial equivalence to the predicate device.

Study that Proves the Device Meets the Acceptance Criteria:

The study conducted to prove the device meets acceptance criteria consists of a series of bench studies and validations.

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

• Sample Size: The document does not specify the exact sample sizes used for each of the bench tests (e.g., how many devices were dynamically fatigued).
• Data Provenance: Not explicitly stated, given it's a physical device and bench testing. It's implied that the testing was conducted by or on behalf of the manufacturer, Straumann USA, LLC (on behalf of Institut Straumann AG), a global company based in Switzerland with operations in the US. The tests are "bench studies," meaning they are laboratory-based, not clinical data from patients.

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

• Not Applicable. This is a physical dental device. "Ground truth" in the context of AI/ML models (e.g., annotated images by experts) is not relevant here. The "truth" is established by adherence to engineering standards and material science principles, as verified through bench testing.

4. Adjudication Method for the Test Set:

• Not Applicable. As no expert review or human interpretation of data (in the AI/ML sense) is mentioned, no adjudication method would be used. The testing is based on objective measurements and conformance to standards.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

• No. This is not an AI/ML-enabled device or an diagnostic imaging device where human readers would interpret results. Therefore, an MRMC study is not relevant.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done:

• Not Applicable. This is a physical medical device (dental implants/bridges), not a software algorithm or AI. While the design uses CAD/CAM software, the "performance" evaluated is that of the manufactured physical product, not the design software itself as a standalone diagnostic tool. The software validation mentioned (IEC 62304) ensures the design software functions correctly, but this is distinct from evaluating an AI algorithm's standalone diagnostic performance.

7. The Type of Ground Truth Used:

• Engineering Standards and Objective Measurements: The "ground truth" for evaluating this device's performance is based on established engineering standards (ISO 14801), regulatory guidance (FDA guidance on dental implants), and scientific principles for material performance. Tests performed include dynamic fatigue, software validation, sterilization validation, and biocompatibility testing. The outcome is whether the device meets the specified performance requirements under these conditions, not a "diagnosis" or "classification" like in an AI context.

8. The Sample Size for the Training Set:

• Not Applicable. This is a physical device, not an AI/ML model. 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. See point 8.

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