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syngo via protoNeo is a software solution intended to be used for viewing, communication, and storage of medical images.

It can be used as a stand-alone device or together with a variety of cleared and unmodified syngo based software options.

syngo via protoNeo supports interpretation of examinations within healthcare institutions, for example, in Radiology, Nuclear Medicine and Cardiology environments.

The system is not intended for the displaying of digital mammography images for diagnosis in the U.S.

syngo®.via protoNeo, version VA10A, is a medical software system that provides tools and features to cover the radiological tasks of reading images. The system receives, stores and distributes images from digital image acquisition devices such as computer tomography and magnetic resonance scanners. The system has workplaces which can be used to review, edit, and manipulate imaqe data, as well as generate quantitative and qualitative data to support an authorized user in diagnosis and treatment planning.

syngo®.via protoNeo version VA10A is a software only medical device. It defines recommended configurations for the hardware to run on. The hardware itself is not seen as a medical device and is not within the scope of this 510(k) submission.

syngo®.via protoNeo is based on a client-server architecture. The server processes and renders the data from the connected modalities. The server provides central services including image processing and temporary storage while incorporating the local database. The client provides the user interface for interactive image viewing and processing and can be installed and stored on each workplace that has a network connection to the server.

The provided document is a 510(k) summary for the syngo®.via protoNeo (Version VA10A) software. This document primarily focuses on demonstrating substantial equivalence to a predicate device (syngo.via VB10A) rather than presenting a detailed study with specific acceptance criteria and performance metrics in the way one might expect for an AI/ML diagnostic algorithm.

Here's an analysis based on the information provided, highlighting what's available and what's missing:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not explicitly provided in the document in the format requested. The document states that "Verification and validation testing confirms that all software specifications have been implemented met the defined acceptance criteria." However, the specific acceptance criteria and the quantitative or qualitative results demonstrating that these criteria were met are not detailed.

The device is a Picture Archiving and Communication System (PACS) software, which is a viewing, communication, and storage solution for medical images. The acceptance criteria would likely be related to aspects like:

• Image display accuracy: Ensuring images are displayed correctly without distortion or loss of information.
• Data integrity: Confirming that images and associated data are stored and retrieved without corruption.
• Performance (speed): Meeting specified requirements for image loading, manipulation, and processing times.
• Functionality: Verification that all features (MPR, MIP, VRT, patient browser, ranges, export) work as intended.
• Compatibility: Ensuring interoperability with various DICOM-compliant devices and operating systems.
• Security: Compliance with cybersecurity requirements.

Since this is a 510(k) for a PACS, the "performance" isn't typically measured by clinical metrics like sensitivity or specificity for disease detection, but rather by the reliable functioning of its intended features.

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

This information is not provided. The document mentions "Non-clinical testing was conducted for the device syngo.via protoNeo during product development" and "The modifications described in this Premarket Notification were supported with verification and validation testing." However, details regarding the specific test sets used, their sample sizes, or their provenance (e.g., country of origin, retrospective/prospective) are absent. These would typically be internal testing datasets.

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

This information is not provided. Given that this is a PACS software and not a diagnostic AI algorithm, the concept of "ground truth" for a test set in the clinical diagnostic sense (e.g., presence or absence of a disease) doesn't directly apply. The testing would likely involve software engineers and quality assurance personnel verifying feature functionality against specifications. If clinical experts were involved, their role would be to validate the utility and accuracy of image display and manipulation features from a clinical perspective, but this is not detailed.

4. Adjudication Method for the Test Set

This information is not provided.

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

A MRMC comparative effectiveness study was not performed or reported. This type of study is specifically designed to evaluate the clinical effectiveness of a diagnostic tool, often an AI algorithm, by comparing human reader performance with and without the tool. The syngo®.via protoNeo is PACS software, not a diagnostic AI algorithm intended to provide primary diagnostic interpretations or assist human readers in a measurable way that would require an MRMC study for this 510(k). The purpose of this submission is to demonstrate substantial equivalence of a general imaging viewing and archiving system.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

This information is not directly applicable and therefore not reported in the context of a standalone diagnostic algorithm. The device, as PACS software, is inherently used by "trained professionals" (clinicians) who evaluate its output. Its performance is about its operational capabilities (displaying, storing, manipulating images) rather than an independent diagnostic decision.

7. Type of Ground Truth Used

The concept of "ground truth" as pathology, outcomes data, or expert consensus for disease detection is not relevant for this type of PACS software submission. The "ground truth" for the non-clinical performance testing would be the pre-defined software specifications and expected functional behavior, which are verified through various tests.

8. Sample Size for the Training Set

This information is not applicable and not provided. The syngo®.via protoNeo is a PACS software system with image viewing and manipulation functionalities based on traditional software engineering principles, not a machine learning or AI algorithm that requires a "training set" in the common sense.

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

This information is not applicable and not provided for the same reasons as #8.

Summary of Device and Evidence provided relevant to the prompt (based on interpretation of PACS software):

The document describes the syngo®.via protoNeo (Version VA10A) as a Picture Archiving and Communication System (PACS) software. Its primary functions are viewing, manipulation, communication, and storage of medical images (CT and MR).

Acceptance Criteria (Inferred from product type and description):

Study Details:

• Sample Size (Test Set) & Data Provenance: Not specified. Testing was "Non-clinical," conducted "during product development." These would be internal verification and validation datasets.
• Number of Experts & Qualifications for Ground Truth: Not specified. Not directly applicable in the sense of clinical 'ground truth' for diagnosis.
• Adjudication Method: Not specified.
• MRMC Comparative Effectiveness Study: Not performed/reported.
• Standalone Study: Not performed/reported in the sense of a standalone diagnostic algorithm. The device performance is its operational functionality.
• Type of Ground Truth: Software specifications, functional requirements, and recognized industry standards (DICOM, various IEC/ISO standards).
• Sample Size for Training Set: Not applicable (not an AI/ML algorithm requiring a training set).
• Ground Truth for Training Set: Not applicable.

This 510(k) is for a foundational software system for medical image management, and the evidence provided aligns with the requirements for such a device, focusing on functional performance, safety, and substantial equivalence to existing similar systems. It does not involve the type of clinical efficacy studies typically required for novel diagnostic algorithms.

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