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This device is a digital radiography/fluoroscopy system used in a diagnostic interventional angiography configuration. The system is indicated for use in diagnostic and anqioqraphic procedures for blood vessels in the heart, brain, abdomen and lower extremities.

INFX-8000V, V6.35, is an X-ray system that is capable of radiographic and fluoroscopic studies and is used in an interventional setting. The system consists of a C-arm, which is equipped with an X-ray tube, beam limiter and X-ray receptor, X-ray controller, computers with system and processing software, and a patient radiographic table.

This document describes a 510(k) premarket notification for the Toshiba Medical Systems Corporation's Infinix, INFX-8000V, V6.35 device. This submission is for a modification to a previously cleared device (INFX-8000V, V6.20, K152696). As such, the information provided focuses on the comparative performance of the modified device against its predicate rather than a comprehensive, standalone clinical study with human readers and ground truth established by experts.

Here's an analysis of the provided text in relation to your questions:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly present a table of "acceptance criteria" against "reported device performance" in the format typically seen for a new device's clinical validation. Instead, it states that the testing demonstrates the modified system's performance is "equal to or better than the predicate system." The performance metrics tested are listed, and the implicit acceptance criterion is that the new version performs at least as well as the predicate.

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

The document does not specify a "test set" in terms of patient images or clinical data. The testing mentioned appears to be phantom or bench testing rather than clinical performance evaluation on human subjects. Therefore, information about data provenance (country, retrospective/prospective) and sample size for clinical test sets is not applicable or provided. The device is tested against applicable standards published by the International Electromechanical Commission (IEC) for Medical Devices and XR Systems.

3. 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)

Since the testing primarily involved phantom/bench testing and not a clinical study with image interpretation, there is no mention of experts being used to establish a ground truth for a clinical test set.

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

Not applicable, as this was not a clinical study involving human interpretation of clinical images.

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 MRMC study was performed or is mentioned. This device does not appear to be an AI-assisted diagnostic tool, but rather an imaging system with technical modifications to its acquisition capabilities and user interface (Virtual ROI, pulse width modulation). The focus is on the performance of the imaging system itself.

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

The testing described (spatial resolution, low contrast resolution, DQE, MTF, etc.) is standalone performance evaluation of the imaging system's technical capabilities, without human in the loop.

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

For the technical performance metrics (spatial resolution, DQE, etc.), the "ground truth" would be established by the physical properties of the phantoms or test objects used and the measurement techniques defined by international standards (IEC). This is not related to expert consensus, pathology, or outcomes data from human patients.

8. The sample size for the training set

Not applicable. This device is an X-ray system, not a machine learning algorithm that requires a "training set" in the context of AI.

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

Not applicable. As stated above, this device does not involve a machine learning training set.

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