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The intended use of this device is retinal photocoagulation and laser trabeculoplasty to be performed by the ophthalmologist.

The OcuLight GL is a semiconductor-based ophthalmic laser photocoagulator which delivers true continuous wave green laser light. It will only be used in conjunction with three fiber optically coupled delivery devices which have received prior FDA-premarket clearance: the IRIS Slit Lamp Adapter (K912918 for retinal photocoagulation and K913430 for laser trabeculoplasty), the IRIS EndoProbe (K894841), and the IRIS Laser Indirect Ophthalmoscope (K903288).

Here's an analysis of the provided text regarding acceptance criteria and supporting studies:

Based on the provided text, it is not possible to describe acceptance criteria or a study that proves the device meets specific acceptance criteria in the way a modern AI/ML device submission would. The document is a 510(k) summary for a medical device (OcuLight GL laser photocoagulator) from 1996, which predates advanced AI/ML algorithms and their associated rigorous validation frameworks.

The document focuses on demonstrating substantial equivalence to existing predicate devices, a common pathway for medical device clearance. This process typically involves showing that the new device has the same intended use, technological characteristics, and safety/effectiveness profile as legally marketed devices, rather than meeting specific performance thresholds against a defined ground truth.

Therefore, many of the requested data points (sample size for test set, data provenance, number of experts, adjudication method, MRMC studies, standalone performance, ground truth for training/test sets, training set sample size) are not applicable or mentioned in this type of 510(k) summary for a hardware-based laser device from that era.

Below is an attempt to structure the available information per your request, with significant caveats where information is missing or not relevant to the context of this device type and submission era.

Acceptance Criteria and Device Performance

Note: The acceptance criteria here are inferred from the 510(k) summary's focus on substantial equivalence. There are no explicit performance metrics (e.g., sensitivity, specificity, accuracy) defined or evaluated against a ground truth as would be expected for an AI/ML device.

Detailed Information (as far as available from the text):

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

   • Not applicable/Not provided. This document does not describe a clinical study with a "test set" in the context of an AI/ML algorithm. The demonstration of substantial equivalence relies on comparing technical specifications and intended use with predicate devices, rather than a clinical trial with a defined patient cohort.

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

   • Not applicable/Not provided. No "test set" or ground truth establishment by experts for performance evaluation is described.

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

   • Not applicable/Not provided.

4. 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 is a hardware device for surgical procedures (laser photocoagulation), not an AI/ML diagnostic or assistive tool for human readers. Therefore, an MRMC study related to readers improving with AI assistance is not relevant or described.

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

   • Not applicable/No. This device is a laser system operated by a human ophthalmologist; there is no "algorithm only" performance to evaluate.

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

   • Not applicable. The concept of "ground truth" for evaluating an AI/ML algorithm's output is not relevant to this type of device submission. The efficacy and safety are deemed equivalent to predicate devices, which would have had their own clinical data or established safe use.

7. The sample size for the training set:

   • Not applicable/Not provided. This device does not involve a "training set" in the context of machine learning.

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

   • Not applicable/Not provided.

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