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The Trinias is an angiographic X-ray system, which is used for diagnostic imaging and interventional procedures. The Trinias is intended to be used for cardiac angiography, neurovascular angiography, abdominal angiography, peripheral angiography, rotational angiography, multi-purpose angiography and whole body radiographic/fluoroscopic procedures.

This notification is for a modified device. The modifications are: Updated user interfaces (wireless mouse, keyboard) A new model of catheterization table A new type of digital system console Additional x-ray tube choices Add alternate choices for the same sizes of digital flat panel detectors An additional size of available flat panel detector (12" x 16") An additional type of control cabinet.

This document is a 510(k) summary for the Shimadzu Trinias angiographic X-ray system. It describes modifications to an existing device and demonstrates substantial equivalence to a predicate device (K123508).

Based on the provided document, the device in question is a medical imaging system (angiographic X-ray system), not an AI/ML-based device. Therefore, the typical acceptance criteria and study designs associated with AI/ML systems (e.g., performance metrics like sensitivity/specificity, multi-reader multi-case studies, ground truth establishment by experts, training/test set provenance) are not applicable here.

The regulatory approval for this device is based on showing substantial equivalence to a previously cleared predicate device, rather than proving performance against specific AI/ML acceptance criteria. The modifications are hardware and software updates to the existing X-ray system.

Here's an analysis of the provided information in the context of device approval, highlighting why AI/ML-specific criteria are not met or relevant:

1. Table of Acceptance Criteria and Reported Device Performance

Not Applicable (for AI/ML performance).

Since this is not an AI/ML device, there are no acceptance criteria related to typical AI/ML performance metrics (e.g., accuracy, sensitivity, specificity, AUC).

The "acceptance criteria" for this submission are compliance with various safety and performance standards for X-ray systems. The reported "performance" is that the modified device meets these standards and is comparable to the predicate.

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

Not Applicable (for AI/ML test set data).

There is no "test set" in the sense of a clinical image dataset used to evaluate an AI algorithm's diagnostic performance. The testing performed was non-clinical bench and standards testing. This involves engineering tests, electrical safety tests, radiation safety compliance tests, and software validation tests.

The data provenance refers to the origin of the device's design, manufacturing, and testing; it does not refer to clinical image data. The manufacturer is Shimadzu Corporation, based in Kyoto, Japan.

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

Not Applicable.

No "ground truth" derived from expert interpretation of medical images was established for this submission, as it's not an AI/ML diagnostic aid. The device's performance is validated against engineering specifications, safety standards, and functional requirements.

4. Adjudication Method for the Test Set

Not Applicable.

Since there's no expert interpretation of a test set, there is no adjudication method.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No. A MRMC study was not done.

MRMC studies are typically performed for AI/ML diagnostic devices to assess how human reader performance (e.g., radiologists) improves with AI assistance compared to without it. This submission is for an X-ray imaging system itself, not an AI-assisted diagnostic tool.

Therefore, there is no effect size of how human readers improve with AI vs. without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

No.

This concept applies to AI/ML algorithms that can produce an output independently. The Trinias is an imaging system; its "performance" is its ability to acquire images, comply with safety standards, and function as intended.

7. The Type of Ground Truth Used

Compliance with regulated standards and functional specifications.

The "ground truth" for this device's approval lies in its adherence to international safety standards (e.g., IEC 60601 series, IEC 62304 for software) and U.S. performance standards (21 CFR 1020.30, .31, .32), as well as verification of its mechanical and electrical functions. This is demonstrated through "bench and standards testing" and "proper system operation is fully verified upon installation."

8. The Sample Size for the Training Set

Not Applicable.

This refers to training data for AI/ML models. The Trinias is a hardware and software system. While its internal software components undergo development and testing, there isn't a "training set" in the AI/ML sense.

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

Not Applicable.

As there is no AI/ML training set, there is no ground truth established for it. Software validation (IEC 62304) and adherence to design specifications guide the software development, but this is distinct from training an AI model.

In summary: The provided document is a 510(k) submission for an updated medical imaging hardware system (X-ray). Its approval focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance and safety testing, and compliance with established regulatory standards. It does not involve AI/ML technology or associated clinical performance studies with diagnostic accuracy endpoints.

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