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OPXION Optical Skin Viewer is a non-invasive imaging system intended to be used for real-time visualization of the external tissues of the human body. The two-dimensional, cross-sectional, three-dimensional, and en-face images of tissue microstructures can be obtained.

OPXION Optical Skin Viewer is composed of two parts consisting of a handheld probe and a mainframe, connected by an optical fiber cable. The device comes with three accessories: a USB 3.0 cable, a power adapter, and a power cord. The Optical Skin Viewer needs to be connected to a laptop or a personal computer. The device uses Optical Coherence Tomography (OCT) technology with a Superluminescent diode, 840 nm, 6 mW light source.

Based on the provided FDA 510(k) clearance letter for the OPXION Optical Skin Viewer, an optical device that visualizes external tissue and is not an AI/ML powered device, the document does not contain the specific information requested about acceptance criteria and a study that proves the device meets the acceptance criteria in the context of AI/ML performance.

The 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (VivoSight Topical OCT System) primarily based on intended use, technology (Optical Coherence Tomography), and general performance (image quality accepted by a qualified medical professional for visualization).

Therefore, I cannot provide a table of acceptance criteria, sample sizes for test sets, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance, or details about training sets, as these specific details are not present in the provided document, nor are they typically required for a Class II medical imaging device like this one unless it incorporates AI/ML for diagnostic or interpretive functions.

However, I can extract the general acceptance criteria and the type of study conducted for this device based on the provided text:

Acceptance Criteria and Study:

The document describes the device's performance in terms of its ability to produce images for visualization, rather than offering specific quantitative metrics for diagnostic accuracy, sensitivity, or specificity that would be typical for an AI/ML driven device.

Here's an interpretation based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Providence

• Test Set Sample Size: The study included three subjects with healthy skin and five subjects with diseased skin conditions.
• Data Provenance: Not explicitly stated, but implies a prospective study given the "Study Design" description of scanning "each target area in three sessions." The country of origin of the data is not specified in the 510(k) summary.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: The document states that "Image quality was confirmed and accepted by a qualified medical professional." This implies at least one, but the exact number beyond "a" is not specified.
• Qualifications of Experts: Described as "a qualified medical professional." No specific specialty (e.g., dermatologist, radiologist) or years of experience are provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated as an adjudication method in the context of multiple readers reaching consensus. The acceptance criterion notes "Image quality was confirmed and accepted by a qualified medical professional," which suggests a single reviewer or possibly an internal review process where consensus was reached without a formal adjudication method described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

• MRMC Study: No, an MRMC comparative effectiveness study was not conducted or described in the provided document. The study focuses on the device's ability to produce images and its substantial equivalence to a predicate, not on how human readers perform with or without the device.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Standalone Performance: This device is an imaging system for visualization, not an AI/ML algorithm that provides diagnostic outputs. Therefore, the concept of "standalone performance" of an algorithm is not applicable or described. Its "performance" is its ability to acquire and display images.

7. The Type of Ground Truth Used

• Type of Ground Truth: The ground truth for this device's performance evaluation was the visual assessment and acceptance of image quality by a qualified medical professional, based on the successful display of "anatomical features of skin" for both healthy and diseased conditions. This is a form of expert consensus/acceptance on display quality.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This is an optical imaging device, not an AI/ML algorithm that undergoes a training phase with a specific dataset.

9. How the Ground Truth for the Training Set Was Established

• Ground Truth for Training Set: Not applicable, as there is no mention of an AI/ML training set.

In summary, the provided FDA 510(k) letter describes a traditional medical imaging device focused on visualization, not an AI/ML-powered device. Therefore, the detailed criteria and study designs typically associated with AI/ML device validation are absent from this document.

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