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NobelActive Zirconia Abutment is a pre-manufactured prosthetic component directly connected to the endosseous dental implant and is intended for use as an aid in prosthetic rehabilitation in the anterior region.

NobelActive Zirconia Abutments are artificial tooth abutments designed to fit and function on the NobelActive Implants. It is an internal hexagonal design similar to the previous SFB (renamed NobelActive) Abutments. The material of construction is the same as the previous Esthetic Zirconia Abutments. The NobelActive Zirconia Abutments differ from the predicate device in trade name and material of construction.

This document is a 510(k) summary for a dental abutment, which is a physical device, not an AI/ML algorithm. Therefore, the questions regarding acceptance criteria, study data, sample sizes, ground truth establishment, expert involvement, and comparative effectiveness studies for AI performance are not applicable.

The document describes the NobelActive Zirconia Abutment, a pre-manufactured prosthetic component intended for use in dental rehabilitation.

Here's the relevant information from the provided text, re-contextualized for a physical medical device:

Device Description:

• Device Name: NobelActive Zirconia Abutment
• Purpose: Artificial tooth abutment designed to fit and function on NobelActive Implants.
• Design: Internal hexagonal design, similar to previous SFB (renamed NobelActive) Abutments.
• Material: Same as previous Esthetic Zirconia Abutments.
• Distinguishing Features (from predicate): Differs in trade name and material of construction from the predicate device.

Indications for Use:

• "NobelActive Zirconia Abutment is a pre-manufactured prosthetic component directly connected to the endosseous dental implant and is intended for use as an aid in prosthetic rehabilitation in the anterior region."

Regulatory Information:

• 510(k) Number: K072129
• Applicant: Nobel Biocare USA LLC
• Predicate Devices: SFB & CFB Angled Abutments (K062749) and Esthetic Zirconia Abutment (K031719)
• Classification: Class II, Endosseous Dental Implant Abutment (21 CFR 872.3630)

Study Information (as pertains to a physical device submission):

For physical medical devices like this, the "study" typically refers to demonstrating substantial equivalence to a predicate device through material characterization, mechanical testing, and biocompatibility assessments, rather than clinical performance studies with large patient cohorts and complex statistical analyses as would be common for new drugs or AI algorithms. The 510(k) process primarily relies on demonstrating that the new device is as safe and effective as a legally marketed predicate device.

The provided text does not contain details about specific acceptance criteria or a study proving the device meets those criteria in the format requested for AI/ML algorithms (i.e., performance metrics, sample sizes for test/training, ground truth, expert opinions, etc.). Instead, the FDA's acceptance is based on the demonstration of substantial equivalence to existing predicate devices.

The submission would have included:

• Comparison to Predicate: A comparison of the new device's technological characteristics (design, materials, indications for use) to the predicate devices.
• Performance Data: This would typically include bench testing for mechanical properties (e.g., strength, fatigue, torque, fracture resistance) of the abutment and its connection to the implant, material characterization (e.g., chemical composition, microstructure), and possibly biocompatibility data. This data would be compared against established standards or performance levels of the predicate devices.
• No "acceptance criteria table" or "reported device performance" in the AI/ML sense is present. The "performance" is implicitly deemed acceptable if it meets the criteria for substantial equivalence, ensuring it performs as intended and is as safe and effective as the predicates.

Without further documentation (e.g., the full 510(k) submission, which would detail the specific tests and their results), it's impossible to provide the granular "acceptance criteria" and "study data" in the AI/ML context.

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