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The Photonics Coldlightsource PL3000 is indicated for use to provide illumination through light-conducting devices such as fiberoptic cables.

The Photonics Coldlightsource PL3000 is a lightsource for fiberoptic cables, with a 230/240 volt version which conforms to ENEC 11, EN60601, EN55011, and UL 2601. The 120 volt version is manufactured to the same requirements of these standards, and has been submitted for approval. Approval is anticipated in the immediate future.

This device is a light source for fiberoptic cables, specifically the Photonics Coldlightsource PL3000. The provided document is a 510(k) Summary, which is a premarket notification to the FDA to demonstrate that the device is substantially equivalent to a legally marketed predicate device. This type of submission generally focuses on comparisons of technical specifications and performance to an existing device rather than a comprehensive "study" in the sense of clinical trials or extensive performance testing found with novel devices or AI/ML-based systems.

Therefore, many of the requested fields, such as sample sizes for test sets, expert ground truth, MRMC studies, or training sets, are not applicable to this type of device and submission. The "study" here is essentially a comparison table to a predicate device and a statement of compliance with relevant standards.

Here's the breakdown based on the provided text:

1. A table of acceptance criteria and the reported device performance

The acceptance criteria are implicitly that the Photonics Model PL3000 performs comparably to or within acceptable limits of the predicate device (Luxtec Model 3150S) for the listed features. The reported device performance is presented as a direct comparison in the table.

The "study" demonstrating this involves a comparison of technical specifications and functional capabilities against a legally marketed predicate device (Luxtec Model 3150S, K890716). The conclusion drawn is that the Model PL3000 is "substantially equivalent" to the predicate device. Additionally, the 230/240 volt version conforms to ENEC 11, EN60601, EN55011, and UL 2601, and the 120-volt version is manufactured to similar requirements.

The following questions are not applicable or cannot be determined from the provided 510(k) summary for this type of medical device (a light source). This summary focuses on establishing substantial equivalence based on technical specifications and indications for use, not on a clinical performance study with patient data or AI/ML algorithms.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not Applicable. This is a hardware device comparison based on specifications, not a test set of data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not Applicable. No ground truth in the context of diagnostic interpretation is established here. The "ground truth" is essentially the documented specifications and performance of the predicate device.

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

• Not Applicable. No expert adjudication of a test set is involved.

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

• Not Applicable. This is a basic medical device (light source), not an AI-assisted diagnostic tool.

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

• Not Applicable. This is a hardware device, not an algorithm.

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

• Not Applicable. The "ground truth" is effectively the established performance and specifications of the predicate device and compliance with international standards mentioned (ENEC 11, EN60601, EN55011, UL 2601).

8. The sample size for the training set

• Not Applicable. This is a hardware device, not a machine learning model.

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

• Not Applicable. This is a hardware device, not a machine learning model.

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