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Image Pasting Application for Revolution XR/d Digital Radiographic Imaging System is indicated for use in generating radiographic images of human anatomy. This device is not intended for mammographic applications.

Device Description

Image pasting allows the operator to generate 2 to 5 sequential radiographic images and electronically join them to create a single electronic mage.

AI/ML Overview

This submission for the GE Medical Systems' Image Pasting application for the Revolution XR/d Digital Radiographic Imaging System (K042602) does not provide explicit acceptance criteria or a detailed study proving the device meets specific performance metrics.

The submission focuses primarily on demonstrating substantial equivalence to a predicate device (Revolution XR/d without image pasting) based on the device's technical specifications, intended use, and adherence to quality standards, rather than direct performance testing.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

Acceptance Criteria	Reported Device Performance
Functional Equivalence	The device electronically joins 2 to 5 sequential radiographic images to create a single electronic image. (Implied: The resulting single image is functionally equivalent to an oversized film radiograph made with multiple exposures.)
Image Quality	Not explicitly stated in terms of specific metrics (e.g., resolution, contrast, artifact levels). The device facilitates the creation of a "single, longer radiograph" for viewing.
Safety (Electrical, Mechanical, Radiation)	Evaluated and conforms with applicable medical device safety standards through independent evaluation.
Compliance with Standards	Conforms with 21 CFR 820, ISO 9001, and ISO 13485 quality systems.
Substantial Equivalence	"Intended uses and other key features are consistent with traditional clinical practice, FDA guidelines, and established methods of patient examination. Intended uses and fundamental scientific technology are the same as the legally market Revolution XRd Radiographic Imaging System."

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

Sample Size: Not applicable. No dedicated "test set" or clinical study data is described for evaluating the image pasting functionality.
Data Provenance: Not applicable.

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

Not applicable. No ground truth establishment activity for a test set is described. The submission relies on the established clinical practice for radiography and the predicate device's performance.

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

Not applicable. No test set or adjudication process 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 was done. This device is an image processing application, not an AI-assisted diagnostic tool, and the submission explicitly states "Clinical Tests: None required."

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

The performance described is inherently "standalone" in terms of the image pasting function's ability to combine images. However, this is a technical function, not a diagnostic algorithm where standalone accuracy would be measured against ground truth. The primary evaluation appears to be technical functionality and safety rather than a diagnostic performance study.

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

Not applicable. No formal ground truth was established for the image pasting functionality, as it's presented as a technical enhancement that mimics existing clinical practice (using oversized film or multiple overlapping exposures). The "ground truth" for the overall system's radiographic image generation would be expert interpretation of the radiographs themselves, but this specific submission focuses on the image manipulation feature.

8. The sample size for the training set:

Not applicable. This is not an AI/machine learning device that would require a "training set" in the conventional sense. The image pasting logic would be based on image processing algorithms.

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

Not applicable, as there is no training set mentioned.

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