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The Obsidian PACS System is used to acquire, transmit, view, and store image or patient data. This data can be transmitted, stored, and viewed over a computer network or off-site using an Internet connection. The typical users of this system are trained professionals, including but not limited to, radiologist, physicians, technicians, and nurses.

The 3D Ultrasound Image Router is indicated for capture and storage of 2D images from an ultrasound system and reconstructing them into 3D ultrasound images. These images provide an approximate representation of the 3D volume for use in obstetric exams. The 3D images are not intended for use in diagnosis or quantitative measurements.

The Obsidian PACS System is comprised of components that connect to an analog or digital medical imaging device (ultrasound, CT, MRI, digitizer), transmit image or patient data, and store image or patient data. The transmission of data can occur internally or externally using a local area network or Internet connection.

The provided text is a 510(k) Summary for the Obsidian PACS System. It describes the device, its indications for use, and a comparison to a predicate device. However, it does not contain information about specific acceptance criteria or a study proving the device meets particular performance metrics.

Here's a breakdown of what is and is not in the provided text, based on your requested information:

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

• Not found. The document states that "The documentation submitted on the device system reflects this level of risk and consist of the following documents: Architectural design chart, Hazard analysis, DICOM conformance statement." It concludes that these documents "contain adequate information and data to enable FDA – CDRH to determine substantial equivalence to the predicate device." This suggests a reliance on design and risk analysis documentation rather than a performance study with specific acceptance criteria.

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

• Not found. There is no mention of a test set, sample size, or data provenance.

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 found. Since there's no mention of a test set or performance evaluation, there's no information about experts or ground truth establishment.

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

• Not found. No test set or human review process is described.

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 found. The device is a PACS system for acquiring, transmitting, viewing, and storing image/patient data, and a 3D Ultrasound Image Router. It is not an AI-assisted diagnostic tool, and therefore, an MRMC comparative effectiveness study to measure human reader improvement with AI assistance would not be applicable or expected for this type of device based on the provided information.

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

• Not found. No standalone performance studies are mentioned. The document explicitly states: "A physician, providing ample opportunity for competent human intervention interprets images and information being displayed and printed." This reinforces that the system is a tool supporting human interpretation, not an autonomous diagnostic algorithm.

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

• Not found. No ground truth is mentioned, as no performance study is described.

8. The sample size for the training set

• Not found. There is no mention of a training set. This is consistent with the nature of a PACS system, which primarily handles data management and display, rather than an AI/ML algorithm that requires training.

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

• Not found. As no training set is mentioned, there's no information on how its ground truth would be established.

In summary: The provided document is a 510(k) Summary for a Picture Archiving and Communication System (PACS) and a 3D Ultrasound Image Router. It focuses on demonstrating substantial equivalence to a predicate device based on technological characteristics, indications for use, and risk assessment (minor level of risk). It does not include a clinical performance study with acceptance criteria, sample sizes, expert involvement, or ground truth establishment relevant to AI/ML device evaluations. The approval is based on documentation such as architectural design, hazard analysis, and DICOM conformance.

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