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Point 4 Translucent Modified is a dental composite restorative material intended to be used in all classes of cavities

The device is a micro-hybrid light cured resin-based dental restorative which contains approximately 79% by weight (59% by volume) inorganic filler but with a particle size nearly half that of Kerr's Herculite XRV and Prodigy. This breakthrough in filler technology provides a higher, longer-lasting polish similar to a microfill, without any negative effects to the physical properties.

This looks like a 510(k) summary for a dental composite restorative material, Point 4 Translucent Modified. The provided text does not contain information about acceptance criteria or a study that proves the device meets those criteria in the way you've outlined for a medical device with performance metrics like sensitivity, specificity, or accuracy.

Instead, this document focuses on establishing substantial equivalence to an already legally marketed predicate device (Kerr Corporation, Prodigy 4 Translucent Shades). For devices seeking substantial equivalence, the "acceptance criteria" are generally met by demonstrating that the new device has the same intended use and similar technological characteristics (e.g., composition, physical properties) as the predicate device, or that any differences do not raise new questions of safety and effectiveness.

Therefore, I cannot fill out your requested table and study details as the information is not present in the provided text. The document does not describe a clinical study or a performance study with specific metrics like those typically associated with diagnostic or AI-driven medical devices.

Here's why the information you're looking for isn't here:

• Device Type: This is a dental restorative material (a composite filling), not a diagnostic device or a device with algorithms that generate performance metrics like sensitivity or specificity. Its "performance" is generally evaluated through its physical and chemical properties, biocompatibility, and clinical handling, rather than algorithmic accuracy.
• Regulatory Pathway: This is a 510(k) submission, which aims to demonstrate substantial equivalence to an existing device. This pathway often relies on comparison to a predicate device's characteristics and established clinical use, rather than requiring novel clinical trials with specific performance endpoints and ground truth adjudication as you've described.
• Missing Information: The provided text only includes a 510(k) summary, a cover letter from the FDA, and an "Indications for Use" statement. It does not include the detailed technical data, test results, or study reports that would typically contain the information you're asking for. These would usually be in other sections of the full 510(k) submission.

If this were a submission for a diagnostic device or an AI-driven medical device, the following would be applicable, but it's not for this particular product based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

• This table would contain metrics like sensitivity, specificity, accuracy, positive predictive value, negative predictive value, AUC, etc., along with the predetermined acceptance thresholds for these metrics. The reported device performance would be the actual results from a clinical or analytical validation study.

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

• For an AI device, this would detail the number of images/cases in the independent test set, and whether they were collected retrospectively or prospectively, and from which geographic regions or institutions.

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

• This refers to the clinicians (e.g., board-certified radiologists, pathologists) who reviewed the test set data to establish the definitive diagnosis or outcome for each case. Their experience level is crucial.

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

• Describes how discrepancies among experts in establishing ground truth were resolved (e.g., two experts agree, or a third expert adjudicates if the first two disagree).

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:

• This would describe a study where human readers interpret cases both with and without AI assistance, allowing for a comparison of their diagnostic performance (e.g., sensitivity, specificity, reading time) and quantifying the AI's impact.

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

• This would detail the performance metrics of the AI algorithm operating independently, without any human input or override.

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

• Explains the "gold standard" used to verify the correctness of the device's outputs (e.g., confirmed pathological diagnosis, long-term patient outcomes, or the consensus of highly qualified experts).

8. The sample size for the training set:

• The number of cases/data points used to train the AI model.

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

• Describes the methodology for annotating and confirming labels for the data used to train the AI (similar to point 7, but for the training phase).

In summary, for the provided document about "Point 4 Translucent Modified," the described information is not applicable because it's a materials science submission based on substantial equivalence, not a performance study for a diagnostic or AI-driven device.

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