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The TOPCON Retinal Camera TRC-NW7SF MARK II is intended for use in capturing images of the retina and the anterior segment of the eye and presenting the data to the eye care professional, with the use of a mydriatic or without use of a mydriatic.
Retina Camera TRC-NW7SF MARK II system is intended for use in capturing images of the retina and the anterior segment of the eye and presenting the data to the eye care professional, with or without the use of mydriatic.

This product is a retinal camera designed to observe, photograph or record the fundus oculi of a patient without coming into contact with the patient's eye and provide as an electronic image the obtained fundus oculi information for subsequent diagnosis. This product comes in two types: a type that capable of both color photography and fluorescein angiography (hereinafter referred to as FA photography); and "Type IA" capable of color photography, FA photography and indocyanine green angiography (hereinafter referred to as IA photography). This product is either equipped with or without an observation monitor used for observation purpose and display of a photographed image. This product comes in three types: a type that houses a digital photography unit for photographing an image; a type to which a commercial TV camera is attached; and a type to which a commercial digital single-lens reflex camera. A digital photography unit, a TV camera and a digital single-lens reflex camera are hereinafter generally referred to as an electronic photography device. A photographed image may be recorded on a commercial memory card built into an electronic photography device, or a connected commercial printer, or a personal computer (hereinafter referred to as a PC).

The provided text describes the 510(k) summary for the TOPCON Retinal Camera TRC-NW7SF MARK II. It does not contain information about acceptance criteria or a study that specifically addresses device performance in terms of clinical accuracy or diagnostic capabilities, especially in a way that would involve sample sizes, expert ground truth, or MRMC studies for AI devices. This submission predates the widespread regulatory evaluation of AI/ML-based medical devices.

Instead, the performance data provided focuses on:

• Conformance to international standards for safety and technical specifications. This is a common approach for traditional medical devices.
• Substantial equivalence to predicate devices. The claim is that the new device performs as safely and effectively as existing, legally marketed devices.

Therefore, many of the requested points cannot be answered based on the provided text.

Here's a breakdown of what can be extracted and what cannot:

1. Table of Acceptance Criteria and Reported Device Performance

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

• The provided document does not describe a test set or data provenance for a clinical performance study as one would typically expect for an AI/ML device. The "performance data" refers to compliance with safety and technical standards, not diagnostic accuracy based on patient images.

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

• Not applicable; no clinical performance study involving expert ground truth is described.

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

• Not applicable; no clinical performance study involving adjudication is described.

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 MRMC study is mentioned. This device is a retinal camera, not an AI-assisted diagnostic tool as described in the context of MRMC studies with AI.

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

• Not applicable; this is a medical imaging device, not an algorithm being evaluated in a standalone capacity for diagnostic performance.

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

• Not applicable; the "ground truth" here is compliance with technical standards and equivalent performance to predicate devices, not clinical diagnostic accuracy.

8. The sample size for the training set

• Not applicable; this is not an AI/ML device that would require a training set in the sense of machine learning algorithms.

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

• Not applicable; no training set for an AI/ML algorithm is described.

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