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FUJIFILM Unity SpeedSuite X-Ray System is Intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position.

The FUJIFILM DR Unity SpeedSuite is a stand-alone X-ray exposure system comprised of a U- arm (manufactured by Sedecal) that incorporates the following: Fuji's image reader equipped with HD Linescan technology, Fuil's CR/DR Console (IIP), and Sedecal's X-ray source and U-Arm system (Verso). Since both are paired, the height and angle can be flexibly changed while maintaining their positional relationship. This system enables you to make an exposure not only at supine or upright position but also at any other desired positions. Images taken by the Unity system are sent to Fuii's IIP (CR/DR Console) to be processed. There is an automatic collimator made by Huestis attached to the tube head. The x-ray tube is manufactured by Toshiba

Here's an analysis of the provided text, focusing on the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided documents, the "acceptance criteria" for the FUJIFILM Unity SpeedSuite are primarily based on demonstrating substantial equivalence to a predicate device, the Sedecal URS LP X-Ray Units with Digital Detector (K042876). The "performance" is implicitly compared through a feature-by-feature assessment rather than explicit quantitative metrics of diagnostic accuracy.

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

The provided documentation does not mention a clinical test set with human patients or images for performance evaluation. The study focuses on demonstrating substantial equivalence through a comparison of technical specifications and the fact that the proposed device combines three already cleared devices.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. No clinical evaluation with expert ground truth establishment is described in these documents. The "ground truth" for this 510(k) submission is the technical specifications and cleared status of the predicate and component devices.

4. Adjudication Method

Not applicable. No clinical evaluation or expert review process requiring adjudication is described.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted or reported in these documents. The submission focuses on substantial equivalence based on technical specifications and the combination of previously cleared components, not on human reader performance with or without AI assistance.

6. Standalone (Algorithm Only) Performance Study

No, a standalone (algorithm only) performance study was not conducted or reported. The device is a hardware system for X-ray acquisition and processing, not an AI algorithm for image interpretation.

7. Type of Ground Truth Used

The "ground truth" implicitly used for this submission is regulatory clearance and established performance of predicate and component devices, along with technical specifications and bench/standards testing results of the proposed device itself. There is no mention of pathology, expert consensus on patient images, or outcomes data.

8. Sample Size for the Training Set

Not applicable. This device is a diagnostic X-ray system, not an AI image analysis algorithm that requires a training set of images.

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

Not applicable. As above, there is no AI algorithm requiring a training set for this device.

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These Radiographic Systems are intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position..

The Rad Vision E is a standard configuration fixed column diagnostic radiographic system. The column can move right or left on a track and the tube head can move up and down. Rad Vision eu is a universal swivel arm X-ray system. It is easy to operate and permits a swift radiographic procedure, a feature which applies to all conventional exposure techniques on all parts of the body. The system is composed of a floor-to-wall column and a turnable arm. On the arm is the tubehead with a collimator mounted to it. All components required for a complete system are supplied. With the patient table, the patient can be moved into any required position without the need for repositioning. Therefore it offers the same advantages as a bucky radiography table. Owing to the large vertical movement of the swivel arm patients in the standing position can be examined from head to feet.

The provided text is a 510(k) summary for the Rad Vision E and Rad Vision eu Diagnostic X-Ray Systems, along with the FDA's clearance letter. This document focuses on the substantial equivalence of the device to a predicate device, rather than a detailed study proving performance against specific acceptance criteria for a novel AI/software medical device.

Therefore, many of the requested elements (like a table of acceptance criteria and proven performance, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC study results, or detailed ground truth information) are not typically found in this type of 510(k) submission for a conventional X-ray system. These aspects are more central to the regulatory submission for AI/ML-driven or image-analysis software devices that require validation of diagnostic accuracy.

The document indicates "The results of bench, test laboratory and clinical testing indicates that the new device is as safe and effective as the predicate devices," but does not elaborate on the specifics of these tests or acceptance criteria beyond general safety and effectiveness in comparison to the predicate.

Here's an attempt to answer the questions based only on the provided text, acknowledging that much of the requested information is not present:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state specific quantitative acceptance criteria or detailed reported device performance metrics in the format of a table you'd expect for an AI/software device. The overarching "acceptance criterion" inferred is that the new device is as safe and effective as the predicate device.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Not explicitly stated in the provided text. The document refers to "clinical testing" but does not provide details on sample size, data provenance, or study design (retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable or not explicitly stated. For a conventional X-ray system submission, the "ground truth" relates more to the physical performance and image quality of the X-ray machine itself, typically evaluated through engineering tests, phantom studies, and possibly clinical trials (without needing a separate expert panel to establish "ground truth" for diagnostic images in the way an AI algorithm would).

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

Not applicable or not explicitly stated.

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 such MRMC comparative effectiveness study is mentioned. This type of study is relevant for AI-assisted diagnostic tools, not typically for the clearance of a conventional X-ray hardware system.

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

This question is not applicable as the device is a diagnostic X-ray system (hardware), not an algorithm or software-only device.

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

For a conventional X-ray system, "ground truth" in terms of diagnostic images is less about establishing a definitive diagnosis on images and more about ensuring the system produces diagnostically acceptable images, measured by objective image quality metrics, dose, and clinical utility. The document does not specify the method for establishing ground truth for any "clinical testing" mentioned.

8. The sample size for the training set

Not applicable. The device is a hardware X-ray system; it does not involve a "training set" in the context of machine learning.

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

Not applicable, as there is no training set for this type of device.

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