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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, V7.0, 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/Ω-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.

The provided text describes a 510(k) premarket notification for a medical device, the Infinix-i, INFX-8000V, V7.0, an image-intensified fluoroscopic x-ray system. The submission focuses on modifications to a previously cleared device (INFX-8000V, V6.35) and asserts substantial equivalence.

Based on the provided information, I can answer some of your questions and explain why others cannot be answered:

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

The document does not explicitly state formal acceptance criteria with specific thresholds or provide a table of reported device performance against such criteria. Instead, it states that "test data demonstrates that the system modifications result in performance that is equal to or better than the predicate system." The evaluation was conducted through "System evaluation of image quality" and "Evaluation of items supported to improve image quality" using phantoms.

The improvements claimed are:

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 mentions that testing was conducted through "bench testing" and "utilizing phantoms" for image quality metrics.

• Sample size: Not specified. The term "phantoms" suggests artificial test objects rather than patient data.
• Data provenance: Not specified, but given the use of phantoms and bench testing, it's likely laboratory-generated data. There is no mention of patient data, clinical studies, country of origin related to patient data, or whether it was retrospective or prospective.

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)

This information is not provided in the document. The testing described focuses on objective image quality metrics using phantoms, not on human expert interpretation of clinical images to establish ground truth.

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

This information is not provided as the testing method described does not involve human adjudication of clinical cases.

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

There is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study, nor is there any indication that the device incorporates AI or is intended to assist human readers in image interpretation. The device is described as an "Image-intensified fluoroscopic X-ray system" with improvements to image processing and hardware.

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

The document describes "System evaluation of image quality" and "Evaluation of items supported to improve image quality" utilizing phantoms. This suggests standalone technical performance testing of the device's image output, without a human in the loop for interpretation in a clinical context. However, it's not an "algorithm only" performance in the sense of an AI model, but rather the performance of the complete imaging system.

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

Given the reliance on "phantoms" for image quality metrics, the "ground truth" would be the known and controlled characteristics of the phantoms themselves (e.g., specific spatial resolution patterns, contrast levels, signal-to-noise ratios). There is no mention of expert consensus, pathology, or outcomes data being used as ground truth.

8. The sample size for the training set

This information is not provided. The document describes an update to an existing X-Ray system, not the development of a machine learning or AI algorithm that typically requires a 'training set'.

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

This information is not provided as there is no mention of a training set or machine learning algorithms in the document.

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