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CRystalView R200 is indicated for use in generating radiographic images of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures.

The Alara CRystalView® R200 is a desktop Computed Radiography (CR) system designed to generate digital x-ray images by reading photostimulable phosphor image plates exposed using standard X-ray systems and techniques. The system consists of a CR Reader, a OC Workstation with software, cassettes, and image plates. Image data is sent via a dedicated connection from the Reader to the CRystalView R200 QC Workstation, where it is processed and displayed for review. The system outputs images and patient information to a PACS using the standard DICOM 3.0 protocol. The fully configured CRystalView R200 System includes acquisition console software and post-processing image enhancement software. A reseller may alternatively provide these two software components or appropriately cleared equivalents, as well as the QC Workstation hardware. The modification reported in this submission replaces the integrated third-party image enhancement software with image enhancement software developed by Alara.

The provided text describes a 510(k) submission for the Alara CRystalView® R200 Computed Radiography System. The submission focuses on demonstrating substantial equivalence to a predicate device after modifying the integrated image enhancement software.

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

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size for Test Set: The text does not explicitly state the specific number of images or cases used in the "confirmatory clinical concurrence study" or "clinical studies." It mentions "images" were judged by a radiologist, but not the quantity.
• Data Provenance: The text does not specify the country of origin of the data. It indicates the study was a "clinical concurrence study," implying prospective data collection for the purpose of comparing the two software versions, but does not explicitly state it was prospective. The context suggests it was to compare the newly implemented software against the previous, implying a comparison on clinical data.

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

• Number of Experts: The text states "a radiologist" was used to judge the images. This implies a single radiologist.
• Qualifications of Experts: The text identifies the expert as "a radiologist." No further details on their experience (e.g., years of experience) are provided.

4. Adjudication Method for the Test Set:

• Adjudication Method: The text indicates that images were "judged by a radiologist." It does not describe any specific adjudication method like 2+1 or 3+1, which are typically used when multiple readers are involved and their opinions differ. Given that only "a radiologist" is mentioned, no complex adjudication method seems to have been applied, or at least it is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size of how much human readers improve with AI vs without AI assistance:

• MRMC Study: No, a multi-reader, multi-case comparative effectiveness study was not explicitly stated. The study described involved "a radiologist" judging images, suggesting a single-reader assessment rather than a multi-reader setup.
• Effect Size of AI Assistance: This study is not evaluating the improvement of human readers with AI assistance. It is comparing the performance of a new image enhancement software (developed by Alara) against an older, third-party image enhancement software, both integrated into a Computed Radiography (CR) system. The core of the study is about the equivalence of image processing software, not AI assistance for human readers.

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

• Standalone Performance: The text does not explicitly describe a standalone (algorithm only) performance study of the image enhancement software. The clinical study involved a radiologist judging images, indicating a human-in-the-loop evaluation of the output of the system (which includes the image enhancement software). The "performance tests" mentioned alongside clinical studies could encompass some standalone technical evaluations (e.g., image quality metrics like spatial resolution, contrast-to-noise ratio), but the text focuses on diagnostic capabilities as judged by a human.

7. The Type of Ground Truth Used:

• Type of Ground Truth: The ground truth for the clinical study was based on expert consensus/opinion (specifically, the judgment of "a radiologist" regarding image quality ratings). There is no mention of pathology, outcomes data, or other objective measures being used for ground truth. The study aimed to assess the equivalence of perceived image quality.

8. The Sample Size for the Training Set:

• Sample Size for Training Set: The text does not provide any information regarding a training set size. The study described is a comparison of image enhancement software versions, not the development or training of a new algorithm requiring a distinct training dataset.

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

• Ground Truth for Training Set: As no training set is mentioned or implied, the method for establishing its ground truth is not applicable or provided in the text.

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