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The Nidek DC-3300 Laser Diode Photocoagulator is indicated for all retinal photocoagulation procedures, such as limited and pan-retinal, transpupillary laser photocoagulation, endophotocoagulation and transscleral photocoagulation, and glaucoma procedures, such as laser trabeculoplasty and iridotomy. The DC-3300 is used in combination with various delivery systems, such as slit lamps, binocular indirect ophthalmoscopes, endoprobes, and transscleral probes.

The DC-3300 Laser Diode Photocoagulator is an 808 nm continuous wave device. The DC-3300 consists of a compact, lightweight main console, footswitch and carrying case. The aiming laser of the DC-3300 is a red diode with a wavelength of 633 nm. A number of delivery systems are available with the DC-3300. A Slit Lamp Delivery Unit (Nidek SL-1600) Multipurpose Delivery Unit for the attachment of slit lamps (Nidek SL-1600), Endophotocoagulation Set, Binocular Indirect Ophthalmoscope (Models A and B), and Transscleral Photocoagulation probes.

The Nidek DC-3300 Laser Diode Photocoagulator is a device for ophthalmic photocoagulation procedures.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

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

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

The provided text does not specify a sample size for a test set in the context of clinical performance data for patients. The performance data described relates to compliance with safety standards and functional specifications of the device itself.

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

Not applicable. The study is based on engineering and safety standard compliance, not clinical ground truth established by experts.

4. Adjudication method for the test set

Not applicable. There is no clinical test set requiring adjudication in the provided information.

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 medical device for laser photocoagulation, not an AI-assisted diagnostic or imaging device requiring human reader assessment.

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

Not applicable. This is a standalone medical laser device, and its performance is evaluated in terms of its technical specifications and compliance with safety standards, not as an algorithm.

7. The type of ground truth used

The "ground truth" for this device's performance is its conformance to established international and national safety standards (IEC 60601-1, IEC 60601-1-2, IEC 60601-1-4, IEC 60601-2-22, IEC 60825-1, UL 2601-1) and its adherence to product specifications and risk analysis findings.

8. The sample size for the training set

Not applicable. This device is not an AI/ML model for which a training set would be required.

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

Not applicable, as there is no training set for this type of device.

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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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