Search Results

The intended use for this device is to serve as support for prosthetic devices to restore patient chewing function.

Steri-Oss 3.25 mm Replace Implant: This device is designed to serve as support for prosthetic devices to restore patient chewing function.
This implant is 3.25 mm in diameter, 10 to 16 mm in length, and is fabricated from titanium allov. They are threaded and have a superior external hex. The threaded portion of the implant body has a 50 micron thick plasma sprayed titanium powder coating, and the surface of the external hex is anodized magenta in color.

The provided text describes a 510(k) submission for a dental implant and explicitly states "Performance Data: Not applicable." This indicates that no specific studies proving the acceptance criteria for the device were conducted or required for this particular submission. The submission relies on claiming substantial equivalence to predicate devices.

Therefore, I cannot provide the requested information about acceptance criteria and a study proving the device meets them because the document clearly states that performance data is not applicable.

However, if this were a different kind of medical device submission where performance data was applicable, here's how I would structure the answer based on your request, using placeholders for the missing information:

Acceptance Criteria and Study to Prove Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: [Number of samples/cases, e.g., "500 images", "100 patient records"]
• Data Provenance: [e.g., "Retrospective, collected from [Country/Countries]] hospitals between [Start Date] and [End Date].", "Prospective, collected from [Country/Countries] in a multi-center study from [Start Date] to [End Date]."]

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

• Number of Experts: [e.g., "3 experts"]
• Qualifications of Experts: [e.g., "Board-certified Radiologists with an average of 15 years experience, specializing in neurological imaging.", "Pathologists with over 10 years of experience in oncological pathology.", "Clinical neurologists with at least 5 years of post-residency experience."]

4. Adjudication method for the test set

• Adjudication Method: [e.g., "2+1 adjudication method, where two experts independently reviewed each case, and a third, more senior expert adjudicated any disagreements.", "3+1 adjudication method, with three independent reviewers and a fourth senior reviewer for discordance.", "Consensus panel of X experts after independent review.", "No adjudication; single expert review."]

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

• MRMC Study Conducted?: [Yes/No]
• Effect Size (if Yes): [e.g., "Human readers' diagnostic accuracy improved by an average of X% (e.g., 5% increase in AUC from 0.85 to 0.90) when using AI assistance compared to without AI assistance (p < 0.001).", "The sensitivity of human readers increased by X% and specificity by Y% with AI assistance."]

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

• Standalone Performance Study Conducted?: [Yes/No]

7. The type of ground truth used

• Type of Ground Truth: [e.g., "Pathology confirmed diagnosis (gold standard).", "Expert consensus among a panel of X specialists.", "Follow-up clinical outcomes data.", "Surgical confirmation.", "Independent imaging modality (e.g., MRI as ground truth for CT)."]

8. The sample size for the training set

• Training Set Sample Size: [Number of samples/cases, e.g., "10,000 images and their corresponding labels."]

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

• Training Set Ground Truth Establishment: [e.g., "Ground truth for the training set was established by a single expert radiologist from de-identified clinical reports and images.", "Automated extraction from electronic health records, followed by expert validation of a subset for quality control.", "Crowdsourcing annotations with a physician oversight for quality checks."]

Ask a specific question about this device