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The Sapheneia Clarity is intended for use by radiologists for transfer, storage, noise reduction, contrast enhancement and viewing of multi-modality images from a variety of diagnostic systems. The device is also intended to be used by trained/qualified technologists for installation and maintenance of the software. For your legal protection, it is strongly recommended that you backup your original data.

For digital mammography, only DICOM 'For Presentation' images should be displayed for primary image diagnosis

The Sapheneia Clarity™ image processing software reduces noise and enhances contrast of relevant structures to increase image quality through structure adaptation, tissue adaptation, scale adaptation, and noise adaptation. Sapheneia Clarity™ employs a sophisticated statistical analysis of the image structure in the neighborhood of each pixel. Using robust estimation methods the dominant structures are separated from the embedding noise. Once the structure has been determined, it is possible to strengthen the interesting parts while simultaneously reducing the noise. The acquisition remains the same, i.e. the image processing can be generated from multiple modalities and with predefined or specific acquisition protocol settings.

The workflow of Sapheneia Clarity™ image enhancement system can be easily adapted to existing radiology departmental workflow. Sapheneia Clarity™ acts as a DICOM node that receives DICOM3.0 digital medical image data from the modality or another DICOM source, processes the data and then forwards the enhanced and/or original study to the selected destination. This destination can be any DICOM node, typically either the PACS system or a specific workstation.

The provided text is a 510(k) summary for the Sapheneia Clarity™ image enhancement system. It does not contain a dedicated performance study with acceptance criteria and reported device performance to demonstrate clinical efficacy or equivalence beyond conformity to standards like DICOM3.0 and JPEG, and substantial equivalence to a predicate device.

Here's an analysis of what is and isn't provided:

1. Table of Acceptance Criteria and Reported Device Performance & 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 & 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Not provided. The document states: "The Sapheneia Clarity™ will successfully complete integration testing, beta testing and verification prior to market release." and "Software development for the Sapheneia Clarity™ system follows documented processes for software design, verification and validation testing." However, details of these tests, specific acceptance criteria for image quality or diagnostic accuracy, and their quantitative results are not included in this 510(k) summary. There is no mention of an MRMC study or a standalone algorithm performance study. The focus is on demonstrating substantial equivalence to a predicate device primarily through intended use, design, and function, rather than through extensive clinical performance data with specific metrics.

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. No specific test set or clinical study data is detailed in this submission.

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. As there's no described performance study or test set with ground truth, this information is absent.

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

• Not provided. No adjudication method is mentioned as there is no specific performance study detailed.

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

• Not provided. No specific ground truth type is mentioned as there is no performance study detailed.

8. The sample size for the training set:

• Not provided. No mention of a training set size, as the document focuses on the device's functionality rather than a machine learning model's training.

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

• Not provided. As there's no mention of a training set, the method for establishing its ground truth is also absent.

Summary of Device and Evidence Presented:

The Sapheneia Clarity™ is an image processing software designed to reduce noise and enhance contrast in medical images from various modalities. It functions as a DICOM node, receiving, processing, and then forwarding images.

The submission primarily relies on demonstrating substantial equivalence to a predicate device, the ContextVision AB, SharpView Image Enhancement System (K024028). The argument for substantial equivalence is based on:

• Intended Use: Both devices are for image transfer, storage, noise reduction, contrast enhancement, and viewing of multi-modality images.
• Design and Function: Sapheneia Clarity™ is described as substantially identical in functionality to certain functions of the predicate, though acknowledging potential slight differences in underlying algorithms for image filtration.
• Performance Standards: The device is designed to conform to DICOM3.0 and JPEG standards.

The document does not report on any specific clinical performance studies with quantitative results, acceptance criteria, test sets, or expert evaluations. It mentions that "The Sapheneia Clarity™ will successfully complete integration testing, beta testing and verification prior to market release," and that "Software development... follows documented processes for software design, verification and validation testing." However, the details and results of these internal tests are not provided in this 510(k) summary.

Therefore, the submission successfully obtained clearance based on substantial equivalence to an existing legally marketed device and compliance with relevant technical standards, rather than through a dedicated clinical study demonstrating specific performance metrics against defined acceptance criteria.

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