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Intended for use by a qualified/trained doctor on both adult and pediatric subjects for obtaining fluoroscopic radiographic images of the skull, spinal column, chest, abdomen, extremities, and other body parts.

The Insight Essentials™ DRF Digital Imaging System is designed to replace the video camera and user interface on the following FDA cleared R&F fluoroscopic imaging systems employing an image intensifier: GE Advantx, GE Legacy, GE P500 and Shimadzu YSF300. The x-ray generator, beam-limiting device and patient positioner. necessary for a full fluoroscopy system are not part of the subject device.

The Insight Essentials™ DRF Digital Imaging System allows the operator to view and enhance high-definition fluoroscopy images. High resolution digital spot images may also be acquired at single or rapid acquisition rates, up to 30 fps. Images may be viewed and enhanced enabling the operator to bring out diagnostic details difficult or impossible to see using conventional imaging techniques. Images can be stored locally for short term storage. The Insight Essentials™ DRF Digital Imaging System enables the operator to produce hardcopy images with a laser printer or send images over a network for longer-term storage. The major system components include: a fluoroscopic video camera, monitors, and an image processor PC.

This document describes a 510(k) submission for the Insight Essentials™ DRF Digital Imaging System, comparing it to a predicate device, the FluoroPRO RF Digital Imaging System (K070390). The submission focuses on demonstrating substantial equivalence rather than proving clinical performance against specific acceptance criteria for an AI algorithm.

Therefore, the requested information regarding acceptance criteria, study details, expert involvement, and ground truth for an AI device is not directly applicable to this submission, as it is a device modification submission for digital imaging system components, not an AI/ML medical device.

However, I can extract information regarding non-clinical tests performed to demonstrate substantial equivalence, which serves as the "study" for compliance in this context.

Here's a breakdown based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance:

Since this is a substantial equivalence submission for hardware/software components, the "acceptance criteria" are implied by the comparison to the predicate device. The goal is to show the new device is "as safe and effective as" the predicate. Performance is assessed through non-clinical bench testing.

The Study:

The study conducted to demonstrate the device meets these (implied) criteria is a non-clinical bench testing study.

2. Sample Size Used for the Test Set and the Data Provenance:

• Sample Size: Not explicitly stated as a number of distinct "cases" or "images" in the way an AI study would define a test set. The document mentions "phantom images" were acquired. The quantity of phantom images is not specified.
• Data Provenance: The images were acquired from a "GE Legacy R&F system" with the subject device installed and compared to images from a "GE Advantx 1 R&F system" with the predicate device installed. This indicates prospective acquisition for the purpose of the comparison. The country of origin is not specified but implicitly within the context of the submitter's (Imaging Engineering, LLC in Florida, USA) operations.

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

• Not applicable in the context of this submission. The "ground truth" for this engineering bench test is objective measurement and comparison of technical parameters and image quality, not expert clinical interpretation.

4. Adjudication Method for the Test Set:

• Not applicable. This was a technical comparison, not a clinical interpretation study requiring adjudication. The analysis was based on direct comparison of acquired images and doses.

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. This is not an AI-assisted device, nor is it a clinical efficacy study involving human readers.

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

• Not applicable. This device is a digital imaging system component, not an autonomous algorithm.

7. The Type of Ground Truth Used:

• The ground truth in this context is objective technical performance measurements (e.g., image output characteristics, resolution, doses, adherence to safety standards) derived from the phantom images acquired during bench testing, compared against the known specifications and performance of the predicate device.

8. The Sample Size for the Training Set:

• Not applicable. This is a hardware/software upgrade to an existing imaging system, not a machine learning model that requires a training set.

9. How the Ground Truth for the Training Set was Established:

• Not applicable, as there is no training set for an AI/ML model.

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