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The System is intended for use in Radiographic/fluoroscopic applications including cardiac, general radiographic/fluoroscopic diagnostic and interventional x-ray imaging for General and Pediations

The Soteria.Al system is classified as an interventional fluoroscopic X-ray system. The fundamental performance characteristics of the Soteria.AI Interventional fluoroscopic X-ray system consists of:

• The patient table and c- arm with X-ray source on one side and the flat panel detector on the opposite side. The c-arm can be angulated in both planes, lifted vertically, shifted to the side, and moved forward/backward by an operator.
• Real-time image visualization of patient anatomy during procedures
• Imaging techniques and tools to assist interventional procedures.
• Post-processing functions after interventional procedures.
• Storage of reference/control images for patient records.
• Compatibility to images of other modalities via DICOM
• Built-in radiation safety controls-with the already FDA-cleared CA-100S / FluoroShield (K182834)

This array of functions provides the physician the imaging information required to achieve minimally invasive interventional procedures.

The Soteria.Al system is available as a Model Al-100 configuration. It is similar to the currently marketed predicate consisting of an X-ray generator, Image processor, collimator, x-ray Tube, Positioner, and patient table with CA-100S / FluoroShield Accessory, (K182834).

Additionally, Soteria.Al can be equipped with an optional X-ray VVA (Vessel and Ventricular Analysis) image analysis (FDA-Cleared) software, (K112807).

The provided text is a 510(k) summary for the Omega Medical Imaging Soteria.AI system. It states that a clinical study was NOT required because substantial equivalence to a predicate device (Omega CS-series-FP with optional CA-100S) was demonstrated through indications for use, technological characteristics, non-clinical performance testing, and safety and effectiveness.

Therefore, the document does not contain information about acceptance criteria or a study proving the device meets acceptance criteria in the context of a clinical performance study. The 510(k) submission primarily relies on non-clinical performance testing and comparison to a predicate device.

However, I can extract information regarding non-clinical performance testing and the grounds for substantial equivalence.

Here's an analysis of the provided information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of direct acceptance criteria coupled with reported device performance in the way one might expect from a clinical study for a new algorithm. Instead, it states that non-clinical performance testing was performed to demonstrate compliance with standards and guidelines, which serve as the implicit "acceptance criteria" for the device's technical and safety characteristics.

2. Sample size used for the test set and the data provenance

• Sample Size for Test Set: Not applicable in the context of a clinical efficacy test. This submission relies on non-clinical performance and safety testing.
• Data Provenance: Not applicable for clinical data. The validation and verification are based on engineering and software testing.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable for a clinical ground truth. The "ground truth" for the non-clinical testing is compliance with established engineering and regulatory standards.

4. Adjudication method for the test set

Not applicable for a clinical test set. The "adjudication" is determined by whether the device adequately passes the defined non-clinical tests and meets specified standards.

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

No, an MRMC comparative effectiveness study was not conducted. The document explicitly states: "The Soteria.AI did not require clinical study data since substantial equivalence to the currently marketed predicate device Omega CS-series-FP was demonstrated..." This device is a fluoroscopic X-ray system, and the AI component (FluoroShield / CA-100S) deals with automated region of interest to reduce exposure, not necessarily an AI for diagnostic interpretation by human readers.

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

The "FluoroShield / CA-100S (K182834)" component, which is an "automated Region of interest that reduces exposure to the patient and operator," functions in a standalone capacity within the X-ray system to manage radiation exposure. The submission states that its software was revised and integrated. However, no specific performance metrics for this AI component in a "standalone" fashion as a diagnostic algorithm are provided, as its role is a safety and optimization feature within the overall imaging system. The submission focuses on its integration and compliance rather than its standalone diagnostic performance.

7. The type of ground truth used

For the non-clinical performance and safety testing, the "ground truth" is defined by:

• Established international and FDA-approved consensus standards (e.g., IEC 62304, ISO 14971, IEC 60601 series).
• FDA guidance documents.
• System requirements specifications (functional, non-functional, safety, privacy, security).
• User and service needs.

8. The sample size for the training set

Not applicable. This document is a 510(k) submission focusing on substantial equivalence through non-clinical testing and updates to an existing system, not a de novo AI algorithm requiring a training set for model development.

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

Not applicable. As no training set was used or described for a new AI algorithm, no ground truth establishment for such a set is detailed.

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