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The PathVision is a Cabinet x-ray system that is used to provide film and/or digital x-ray images of harvested specimens from various anatomical regions in order to provide rapid verification that the correct tissue has been excised during the biopsy procedure. Doing the verification directly in the same room or nearby enables cases to be completed faster, thus limiting the time the patient needs to be under examination. Specimen radiography can potentially limit the number of patient recalls.

The Faxitron PathVision Specimen Radiography System is a Cabinet X-ray System specifically designed to provide high detail radiographic imaging of surgically excised medical specimens. The exceptionally high magnification capability (up to 6X) from the 0.02 mm focal spot with optimized cabinet geometry and the superior contrast available from the low kV capability provides enhanced film and/or digital imaging performance. This device supports radiographic film sizes up to 35 x 35 cm and can be configured to acquire high resolution, DICOM compliant, digital x-ray images up to 23 x 29 cm in size through the use an integrated camera and Faxitron Specimen Radiography software.

The provided text is a 510(k) Pre-market Notification for the Faxitron PathVision Specimen Radiography System. It describes the device, its intended use, technical specifications, and declares substantial equivalence to predicate devices. However, this document does not contain information about acceptance criteria or a study proving the device meets acceptance criteria in the context of AI/machine learning performance.

The document focuses on the hardware specifications, safety compliance, and equivalence to existing x-ray systems. It's a standard pre-market notification for a medical device that uses X-ray technology, not an AI or software algorithm.

Therefore, I cannot fulfill your request for the following information as it is not present in the provided text:

1. A table of acceptance criteria and the reported device performance: This document does not define performance metrics or acceptance criteria for an AI or software algorithm. It describes physical characteristics and compliance with X-ray system standards (e.g., 21 CFR 1020.40).
2. Sample sized used for the test set and the data provenance: Not applicable, as there is no mention of a test set for a software algorithm.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
4. Adjudication method: Not applicable.
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: Not applicable. The device is a specimen radiography system, not an AI-assisted diagnostic tool for human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
7. The type of ground truth used: Not applicable.
8. The sample size for the training set: Not applicable.
9. How the ground truth for the training set was established: Not applicable.

The "software" mentioned in the document refers to system control software, image acquisition software, and DICOM compliance for image management, not a diagnostic AI algorithm.

In summary, the provided document does not support a description of acceptance criteria and a study proving an AI device meets those criteria because the device in question is a traditional X-ray specimen radiography system, not an AI/ML-based diagnostic system.

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