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This device is intended for high resolution hard copy imaging of digital image source material (DICOM). The hardcopy output includes however is not limited to, digital radiography, nuclear medicine, ultrasound, CT, MRI, CR and Radiation Therapy planning; Images are suitable for medical image diagnosis use and referral. The system is intended for use by medical radiologists, imaging modality specialists, and communications to referring physicians. Not for mammography use.

CYPHER implements the necessary DICOM services to receive DICOM print jobs and provides an interface for printing the received data on a Windows™ printer. The device is designed for use with the CANON imagePRESS™ C1 Digital Print System. It consists of an Intel® 945GSE Mini-ITX Board in a shielded enclosure. The user interface is implemented on either an attached monitor or via the remote desktop function inherent in Windows XP. Power is supplied via an external UL listed 12 volt power supply.

The provided text describes a medical image hardcopy device, the "Cypher DICOM Print Solution," and its FDA 510(k) summary. However, it does not contain the detailed information necessary to answer all sections of your request regarding acceptance criteria and the comprehensive study proving its performance.

The document is a regulatory submission for substantial equivalence to a predicate device, focusing on its DICOM compatibility and ability to produce high-quality hardcopy printouts. It mentions "Performance Testing/Data" that demonstrated the device is "safe and effective, performs comparably to and is substantially equivalent to the predicate device," and that "Tests include: Software Validation and evaluation of hardcopy output." Beyond this general statement, specific acceptance criteria, detailed study design, sample sizes, expert qualifications, or ground truth methods are not provided.

Here's a breakdown of what can and cannot be answered based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document only states that the device meets these general criteria but does not provide specific quantitative metrics or results that would typically constitute detailed acceptance criteria (e.g., specific resolution targets, color accuracy thresholds, DICOM conformance test results with pass/fail ratios).

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

• Not provided. The document states "Tests were performed on the device," but gives no details about the size or nature of the test set, nor the provenance of any data used for evaluation.

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 provided. While the device is intended for "medical image diagnosis use and referral" by "medical radiologists, imaging modality specialists," the document does not mention the involvement of experts in establishing ground truth for any performance testing.

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

• Not provided. The document does not describe any adjudication method, suggesting that such a method was either not relevant to the type of testing performed (e.g., functional and imaging quality evaluation for a printer) or not considered necessary for this 510(k) submission.

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, an MRMC study was not done, nor would it be relevant for this device. This device is a "Medical Image Hardcopy Device (Printer)," not an AI-powered diagnostic tool. Its function is to print DICOM images, not to assist in image interpretation or diagnosis directly via AI. Therefore, an MRMC study measuring human reader improvement with AI assistance is not applicable.

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

• Yes, in the sense that the "device" (printer) was tested independently. The performance testing mentioned ("Software Validation and evaluation of hardcopy output") would inherently be a standalone evaluation of the printer's output quality and DICOM conformance. However, this is not an "algorithm only" in the context of an AI device, but rather a functional evaluation of a hardware/software system.

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

• Not explicitly stated in terms of diagnostic ground truth. For a printer, the "ground truth" would likely involve objective measures of print quality, color accuracy, resolution, adherence to DICOM standards for rendering, and potentially comparison to the digital image data itself. It's not about clinical diagnosis ground truth (like pathology or patient outcomes).

8. The sample size for the training set

• Not applicable / Not provided. This is a medical image printer, not an AI model requiring a training set in the conventional sense. The "training" would be more akin to software development and testing, not machine learning model training.

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

• Not applicable / Not provided. See point 8.

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