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The PicomEnterprise software is intended for acceptance, transfer, display, storage and digital processing of medical images.

Its hardware components may include digitizers, workstations, communications devices, computers, video monitors, magnetic, optical disk, or other digital data storage devices and hardcopy devices.

The software components provide functions for performing operations related to image manipulation, enhancement, compression or quantification.

To support the diagnostic interpretation of Mammography studies, PicomEnterprise will display the full fidelity DICOM image in a non-compressed format. Images will be rendered with patient and clinical information clearly displayed as part of the DICOM Overlay as required by MQSA, on monitors cleared by FDA for use in Digital Mammography. Lossy compressed mammography images and digitized film screen images should not be used for the purpose of primary diagnosis. Mammographic images may only be interpreted using an FDA approved monitor that offers at least five megapixel resolution and meets other technical specifications reviewed and accepted by FDA.

The PicomEnterprise software is a multi-modality comprehensive two-, three- and four-dimensional image presentation software system intended for acceptance, transfer, display, storage and digital processing of medical images. The PicomEnterprise software combines reconstruction and display algorithms for medical image analysis in the familiar Microsoft Windows environment. PicomEnterprise offers full compliance with DICOM 3.0 standard that permit transfer of data from medical devices to storage server and then to other DICOM compliant devices.

The provided information does not contain details about acceptance criteria or specific studies proving the device meets them. The document is a 510(k) Premarket Notification for the Scimage PicomEnterprise software, primarily focusing on its intended use, description, and comparison to predicate devices for FDA clearance.

Here's an analysis of what is provided in relation to your request:

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

   • Not provided. The document describes the device's functions (acceptance, transfer, display, storage, digital processing of images, image manipulation, enhancement, compression, quantification) and its compliance with standards like DICOM 3.0 and JPEG. It also specifies its use for mammography studies, including displaying full-fidelity DICOM images on FDA-cleared 5-megapixel monitors. However, it does not state any specific performance metrics (e.g., accuracy, speed, uptime) or associated acceptance criteria that were met through testing.

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. There is no mention of any specific test set, its size, or the provenance of any data used for testing.

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. Since no specific test set or ground truth establishment is described, details about experts are absent.

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

   • Not provided. No adjudication method is mentioned.

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

   • Not provided. This document predates widespread AI/ML in medical devices and focuses on PACS software. No MRMC study or AI assistance is mentioned.

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

   • Not provided. The device is a "Medical image workstation system, PACS," which implies a human operator. There's no mention of a standalone algorithm performance study.

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

   • Not provided. As no performance study with a test set is discussed, the type of ground truth is not specified.

8. The sample size for the training set

   • Not provided. The document describes a software system, not a machine learning model that would require a distinct "training set."

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

   • Not provided. Similar to point 8, this is not applicable given the nature of the device described.

In summary, this 510(k) filing is for a Picture Archiving and Communications System (PACS) software. Such devices, especially around the 2007 timeframe of this filing, were typically cleared based on substantial equivalence to predicate devices, compliance with established standards (like DICOM), and functional verification, rather than clinical performance studies using acceptance criteria as might be expected for novel diagnostic algorithms. The document confirms that the device is "substantially equivalent" to predicate devices and complies with voluntary standards like ACR/NEMA DICOM and JPEG.

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