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510(k) Data Aggregation
(22 days)
The Self-Compressive Screws are intended for the fixation of bone reconstruction in the hand and in forefoot surgery.
The Self-Compressive Screw consists of screws available in several diameters and lengths. All the implants are made of titanium alloy. The fixation is provided thanks to the threading of the screw, which allows compression.
The provided document is a 510(k) premarket notification for a medical device called the "Self-Compressive Screw". It primarily focuses on demonstrating substantial equivalence to predicate devices rather than presenting a study with acceptance criteria for the device itself.
Therefore, this document does not contain the information required to answer your request about acceptance criteria and a study that proves the device meets those criteria, especially in the context of AI/software performance.
Here's why and what information is available:
- Device Type: The device is a "Smooth or threaded metallic bone fixation fastener," which is a physical implant, not a software or AI-driven diagnostic or treatment device.
- Study Type: The document explicitly states "No clinical studies were performed." and primarily relies on non-clinical mechanical testing to demonstrate equivalence.
- Focus of Submission: The entire submission is about demonstrating "substantial equivalence" to legally marketed predicate devices, meaning the new device is as safe and effective as existing ones. It's not about proving novel performance against acceptance criteria for a new type of capability.
However, I can provide the mechanical tests performed, which could be interpreted as a form of performance evaluation for this type of device:
1. A table of acceptance criteria and the reported device performance
Based on the document, the "acceptance criteria" are implied by the performance of the predicate device. The new device is considered "equivalent" if its mechanical performance is comparable to the predicate. No explicit numerical acceptance criteria are stated in the provided text, but the tests performed are:
| Test Name | Standard Used | Reported Performance | Acceptance Criteria (Implied) |
|---|---|---|---|
| Resistance to torsion | ASTM F543 – Annex 1 | Equivalent to predicate device | Performance comparable to predicate device |
| Pull-out strength | ASTM F543 Annex 3 | Equivalent to predicate device | Performance comparable to predicate device |
2. Sample size used for the test set and the data provenance
The document does not specify the sample size used for the mechanical tests, nor does it provide details on the provenance of data for these non-clinical tests (e.g., country of origin, retrospective/prospective).
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 mechanical device, and "ground truth" in the context of expert review for medical images or diagnoses is not relevant here. The "ground truth" for mechanical testing is established by the physical testing results themselves.
4. Adjudication method for the test set
Not applicable. This concept applies to expert review of data, not to mechanical testing of a physical device.
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
No. This type of study is not relevant for a metallic bone fixation fastener and was not performed.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
No. This concept applies to software/AI algorithms and is not relevant for this physical device.
7. The type of ground truth used
For the mechanical tests, the "ground truth" is the quantitative measurement of mechanical properties according to established ASTM standards. This is not "expert consensus," "pathology," or "outcomes data."
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" as this is not an AI/ML device.
In summary, the provided document is for a physical medical device and primarily uses non-clinical mechanical testing and a comparison to predicate devices to demonstrate substantial equivalence, rather than detailing an AI/software performance study with specific acceptance criteria as you requested.
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