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The Imation™ SE-204 laser imager is intended use as a high resolution hard copy device for output from digital imaging source modalities for use in medical imaging diagnosis and referral. Electronic image information signals are managed in the SE-204 and transformed optically to expose Imation imaging media. The system is intended for use with a variety of digital modalities, including, but not limited to Digital Radiography and full view digital mammography for diagnostic use by medical radiologists and communications to referring physicians and their patients.

The Imation™ SE-204 laser imager is intended use as a high resolution hard copy device for output from digital imaging source modalities for use in medical imaging diagnosis and referral. Electronic image information signals are managed in the SE-204 and transformed optically to expose Imation imaging media. The system is intended for use with a variety of digital modalities, including, but not limited to Digital Radiography and full view digital mammography for diagnostic use by medical radiologists and communications to referring physicians and their patients.

The subject device and predicate devices use the same technical design base. The printers receive image data from the modality. Modality data and printing functions are performed by the IMS(Image management System). User control is performed by a keypad or directly by the modality through the host control. Imation imaging media is removed from a daylight cartridge and transported to the laser imaging station. Image data and media merge at the laser station and the film is scanned. The exposed media is transported through the integrated processor and exits the printer.

Software is used to control the image management and machine functions. AIQC(Automated Image Quality Control) matches printing power with film characteristics to provide consistently high image quality.

The provided text describes a 510(k) summary for the Imation™ SE-204 Laser Imager, a hard copy device for medical imaging. The focus of this submission is on demonstrating substantial equivalence to predicate devices, rather than a detailed artificial intelligence (AI) performance study. As such, information regarding AI-specific acceptance criteria, study design, and performance metrics (like F1 score, effect size in MRMC studies, etc.) is not present in the provided document.

The document primarily discusses the device's technical characteristics, intended use, and its equivalence to predicate devices in terms of spatial resolution and safety standards.

Here's an analysis of the provided information based on your request, highlighting what is present and what is missing due to the nature of the device and the submission:

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

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 describes internal and field tests but does not specify sample sizes for image data or the origin/nature of that data. Given the device is a laser imager, the "test set" would likely refer to images processed and printed.

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 / Not Provided. For a laser imager, "ground truth" would relate to the fidelity of image reproduction, not diagnostic accuracy. The document mentions "medical personnel" reviewing images, implying human oversight, but no specific experts or their qualifications for establishing ground truth for testing the imager are described.

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

• Not Provided / Not Applicable. Adjudication methods are typically for diagnostic accuracy studies, which is not the primary focus of this device's 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. This is not an AI-assisted diagnostic device. Therefore, no MRMC study comparing human readers with and without AI assistance was performed or reported.

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

• Not Applicable. This device is a laser imager, not a diagnostic algorithm; thus, "standalone" AI performance is not relevant. The device's "AIQC (Automated Image Quality Control)" is part of its internal software function, ensuring consistent output, not making diagnostic decisions.

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

• Implied Technical Ground Truth. For a laser imager, the "ground truth" for performance evaluation would generally be the fidelity of the printed image to the digital input signal, in terms of resolution, density, and freedom from artifacts. This is assessed through objective measurements and visual inspection against known test patterns and objective physical standards rather than clinical outcomes or diagnostic interpretations. The document mentions "test pattern generator" and "AIQC" to ensure consistency.

8. The sample size for the training set

• Not Applicable / Not Provided. This is a hardware device with an internal quality control system (AIQC), not a machine learning model that requires a "training set" in the conventional sense for diagnostic tasks. The AIQC likely involves calibration and control mechanisms, not a learning algorithm trained on a dataset for prediction.

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

• Not Applicable / Not Provided. As above, a "training set" in the AI sense is not specified. The "ground truth" for the AIQC system would be defined by engineering specifications for image quality and consistency.

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