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The Angio Workstation (XIDF-AWS801) is used in combination with an interventional angiography system (Infinix-i series systems and INFX series systems) to provide 2D and 3D imaging in selective catheter angiography procedures for the whole body (includes heart, chest, abdomen, brain and extremity).

When XIDF-AWS801 is combined with Dose Tracking System (DTS), DTS is used in selective catheter anglography procedures for the heart, chest, abdomen, pelvis and brain.

The XIDF-AWS801 Angio Workstation is used for images input from Diagnostic Imaging System and Workstation, image processing and display. The processed images can be outputted to Diagnostic Imaging System and Workstation.

The provided text describes a 510(k) premarket notification for the Toshiba XIDF-AWS801, Angio Workstation, V6.20. It primarily focuses on demonstrating substantial equivalence to a predicate device (XIDF-AWS801, Angio Workstation, V6.10) after modifications.

Here's an analysis of the acceptance criteria and the study information based on the provided text:

Acceptance Criteria and Reported Device Performance

The text does not explicitly list quantitative acceptance criteria with specific thresholds for device performance. Instead, it states that the testing demonstrated that the "implementation of the modifications retained the safety and effectiveness of the cleared device" and that the "modifications result in performance that is equal to or better than the predicate system." This implies that the acceptance criteria were based on maintaining or improving upon the performance of the predicate device.

The modifications introduced are:

• Dynamic Device Stabilizer function: Provides real-time multi-frame image display during live fluoroscopy or DA, which makes a stent appear stationary.
• 3D-LD image display: 3D-DA performed at low dose. This is intended for use in high-contrast vascular imaging studies (e.g., pediatric pulmonary arteriography) and confirming therapeutic effectiveness immediately after IVR. The text explicitly notes that "this technique should not be used for definitive diagnosis or for measurement."

Table of Acceptance Criteria and Reported Device Performance (Inferred):

Detailed Study Information:

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

   • The text mentions "archived image data sets" were used for testing. However, it does not specify the sample size of these data sets.
   • The data provenance (country of origin, retrospective/prospective) is not specified.

2. 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):

   • The document does not mention the use of experts or how ground truth was established for the test set. The testing primarily focused on technical performance and comparison to the predicate, rather than diagnostic accuracy involving human interpretation and ground truth.

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

   • The document does not describe any adjudication method as expert review or diagnostic accuracy studies were not detailed.

4. 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 done or at least not described in this document. The device is an angiography workstation with enhanced display and stabilization features, not an AI diagnostic assistant tool in the context of improving human reader performance.

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

   • The testing described was for the "modified system" and involved "bench (phantom) tests, simulations and archived image data sets." This implies a standalone performance evaluation of the new features (Dynamic Device Stabilizer and 3D-LD image display) to ensure they functioned as intended and maintained the overall performance of the workstation. However, "standalone" in the AI sense (algorithm-only diagnostic output) is not applicable here as this is a processing and display workstation, not a diagnostic algorithm.

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

   • The document does not explicitly state the type of "ground truth" used. Given the nature of the modifications (image stabilization and low-dose 3D display), the "ground truth" for performance would likely relate to objective image quality metrics, successful stabilization during phantom tests, and verification that the 3D-LD acquisition produced usable high-contrast images as intended, rather than clinical diagnostic ground truth like pathology.

7. The sample size for the training set:

   • The document does not mention a "training set" as this is a medical device incorporating new processing and display functions, not a machine learning model that undergoes a distinct training phase with a dedicated dataset.

8. How the ground truth for the training set was established:

   • This is not applicable as there is no mention of a training set for an AI/ML model.

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