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The Marosis™ PACS System is a device that receives digital images and data from various sources (including but not limited to CT scanners, MR scanners, ultrasound systems, R/F Units, computed & direct radiographic devices, secondary capture devices, scanners, imaging gateways or other imaging sources). Images and data can be stored, communicated, processed and displayed within the system and or across computer networks at distributed locations.

The Marosis™ PACS product handles various objects in a Picture Archive and Communication System (PACS) environment. These objects can be images, requests, patients, examination etc. Marosis™ PACS transmits digital electronic images and generates reports over a high-speed network to centralized storage. After transmission, patient information and images are available throughout the facility to many users simultaneously.

This 510(k) summary (K012844) describes the Marosis™ PACS (Picture Archiving Communications System). As a PACS system, its primary function is to receive, store, communicate, process, and display digital medical images and data.

Here's an analysis of the provided text regarding acceptance criteria and the study:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not contain any explicit acceptance criteria or specific performance metrics for the Marosis™ PACS system. It focuses on demonstrating substantial equivalence to a predicate device (MEDIFACE™ PACS, K010259) rather than presenting quantitative performance data against predefined criteria.

The submission states: "The 510(k) Pre-Market Notification for Marosis™ PACS contains adequate information and data to enable FDA - CDRH to determine substantial equivalence to the predicate device." This implies the "acceptance" was based on demonstrating similar functionality and safety profiles to the predicate, rather than specific numerical performance targets.

2. Sample Size Used for the Test Set and Data Provenance

The document does not mention any specific test set, sample size, or data provenance (e.g., country of origin, retrospective/prospective). The submission relies on a comparison to a predicate device, which usually involves a review of the predicate's technical specifications and intended use, rather than a new comparative study with a dedicated test set for the new device.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

No information is provided regarding experts used to establish ground truth. As no specific test set or clinical study is described, there would be no need for expert ground truth establishment for a new device's performance.

4. Adjudication Method for the Test Set

No adjudication method is mentioned as no specific test set or clinical study requiring adjudication is described.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study is mentioned. The submission focuses on substantial equivalence to a predicate device based on its technical characteristics and indications for use.

6. Standalone Performance Study

No standalone performance study is explicitly described. The document emphasizes that "A physician, providing ample opportunity for competent human intervention interprets images and information being displayed and printed." This highlights the human-in-the-loop nature of interpretation with a PACS system, rather than a standalone AI algorithm producing diagnoses.

7. Type of Ground Truth Used

No specific type of ground truth is mentioned, as the submission does not detail a clinical study where ground truth would be established for measuring diagnostic accuracy or similar performance metrics.

8. Sample Size for the Training Set

No information is provided regarding a training set sample size. PACS systems, like the one described, are primarily software platforms for managing and displaying existing medical images. They are not typically AI/ML systems that undergo "training" in the traditional sense of learning from a dataset to perform specific diagnostic tasks.

9. How the Ground Truth for the Training Set Was Established

Not applicable, as no training set or AI/ML model requiring ground truth for training is described.

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