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The PathVisionXL 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 PathVisionXL is specially designed for high detail radiographic imaging of surgically excised medical specimens. It is a fully shielded Cabinet X- ray System that has been designed to comply with 21 CFR 1020.40. The larger (XL) cabinet allows up to 10.0 times geometric magnification of excised specimens with minimal geometric distortion through the use of a focal spot size that is less than 15 microns. With optimized cabinet geometry and the superior contrast available from the low kV capability, the PathVisionXL provides enhanced image performance. It is designed to acquire a large high resolution digital images up to 43 x 43 cm in size, through the use an integrated digital X-Ray detector and Faxitron Vision software. The Faxitron Software supports the DICOM Store, Print and Modality Worklist services.

The provided text does not contain information about acceptance criteria for a specific device performance, nor does it detail a study proving such criteria are met. The document is an FDA 510(k) premarket notification for the PathVisionXL, focusing on its substantial equivalence to a predicate device.

Therefore, I cannot provide the requested table and study details. The document primarily addresses the safety, technological characteristics, and regulatory compliance of the PathVisionXL as a cabinet x-ray system for specimen radiography.

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The Faxitron VisionCT is a Cabinet x-ray system that is used to provide two and three dimensional 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 VisionCT is specially designed for high detail radiographic imaging of surgically excised medical specimens. It is a fully shielded Cabinet X- ray System that has been designed to comply with 21 CFR 1020.40. It allows up to 4.0 times geometric magnification of excised specimens with minimal geometric distortion through the use of a focal spot size that is less than 15 microns. With optimized cabinet geometry and the superior contrast available from the low kV capability. the VisionCT provides enhanced image performance. It is configured to acquire high resolution digital images up to 15 x 15 cm in size, through the use an integrated detector and Faxitron Vision Specimen Radiography software. The Faxitron Software supports the DICOM Store, Print and Modality Worklist services.

Here's an analysis of the provided text to extract information about the acceptance criteria and the study proving device performance:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state formal acceptance criteria in a quantitative table format for the device's diagnostic performance (e.g., sensitivity, specificity for detecting specific tissue types). Instead, it focuses on technical specifications and comparability to predicate devices. The "reported device performance" is primarily descriptive of its capabilities rather than a set of performance metrics against specific targets.

However, based on the non-clinical testing data mentioned, we can infer some "performance" aspects:

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

The document states: "The non-clinical performance testing data provided in this submission includes detector Xineos 1515 imaging performance data and sample clinical images to demonstrate the capability of the VisionCT to image various anatomical regions (breast tissue, femoral head, other tissues such as fallopian tube, prostate, nasal polyp, and heart valve)."

It also mentions: "The phantom images include those provided to compare the 2D images from the predicate device and 3D images from the subject device. In addition, a uniform phantom and phantom with embedded test objects with ranges of sizes and contrasts designed to characterize CT x-ray system contrast and detail imaging performance, are also provided."

• Test Set Sample Size:
  • Clinical Images: The exact number of "sample clinical images" is not specified. It's described generally as "sample clinical images... to image various anatomical regions." This implies a limited set of images from different tissue types.
  • Phantom Images: Not specified, but includes a "uniform phantom and phantom with embedded test objects."
• Data Provenance: Not specified in the provided text. It does not mention the country of origin or whether the data was retrospective or prospective for the clinical images. Given the context of a 510(k) summary, these are likely internal validation studies.

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

The document does not specify the number of experts used or their qualifications for establishing ground truth on the "sample clinical images." The ground truth for these images would likely involve pathology or surgical confirmation of tissue excision, but this is not detailed.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method (e.g., 2+1, 3+1, none) for a diagnostic interpretation of the test set. The focus is on the device's ability to provide images and its technical performance, not on a human interpretation study.

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 described.
• The device is a specimen X-ray system providing 2D and 3D images for rapid verification of excised tissue during biopsy. It is not an AI-assisted diagnostic device, and therefore, no comparison of human readers with or without AI assistance is mentioned or relevant to this submission.

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

• A standalone performance evaluation of the imaging system was done. The "non-clinical performance testing data provided" focuses on the device's technical specifications, imaging capabilities (2D and 3D), image quality using phantoms, and artifact analysis. This testing demonstrates the algorithm's (and hardware's) ability to produce images and reconstruct 3D data independently.
• However, it's crucial to distinguish this from an "algorithm only" performance for a diagnostic task. The device itself provides images; the human still performs the "verification that the correct tissue has been excised." The standalone performance here refers to the system's ability to generate high-quality images, not its ability to make a diagnostic call itself.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• For the "sample clinical images," the implicit ground truth for verifying "correct tissue has been excised" would likely be pathology results or surgical confirmation that the specimen indeed represents the intended tissue. However, this is not explicitly stated.
• For the phantom images, the ground truth is the known characteristics of the phantom (e.g., sizes and contrasts of embedded test objects).
• For electrical safety and EMC, the ground truth is compliance with established international standards (IEC 61010 series, IEC 61326 series).

8. The Sample Size for the Training Set

The document does not mention a training set in the context of machine learning or AI. The Faxitron VisionCT is an imaging device, not an AI-based diagnostic algorithm that requires a separate training set. The "Faxitron Vision Software" mentioned is an operating software for the device, upgraded from a predicate, and was "thoroughly tested," but not in the sense of a machine learning training process.

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

As no training set (for machine learning) is discussed, this question is not applicable.

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The VersaVision 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 VersaVision 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 8X) from the

The provided text is a 510(k) premarket notification for the Faxitron VersaVision device. It focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a study proving that the device meets specific acceptance criteria in the context of clinical performance (e.g., diagnostic accuracy for an AI/CAD system).

Instead, the document details the technical specifications, intended use, and nonclinical performance data testing against regulatory standards for an X-ray system. It does not describe a study involving specific acceptance criteria for diagnostic performance, sample sizes for test sets, ground truth establishment, or human reader performance.

Therefore, many of the requested items cannot be extracted from this document as they are not applicable to the type of submission presented.

However, I can extract information related to the device's technical specifications and compliance with regulatory standards.

Here's the closest interpretation of your request based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't define "acceptance criteria" in terms of clinical performance metrics (like sensitivity, specificity, etc.) for a diagnostic study. Instead, it refers to compliance with performance standards for X-ray systems.

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The BioVision (Plus/ +)Digital Specimen Radiography (DSR) System is a cabinet digital X-ray imaging system intended to generate and control X-rays for examination of harvested specimens from various anatomical regions, and to provide rapid verification that the correct tissue has been excised.

Performing the verification directly in the same biopsy procedure room 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. This device is intended to be operated wherever the medical professionals deem appropriate, including a surgical suite or a room adjacent to a surgical suite.

The BioVision(Plus) Digital Specimen Radiography (DSR) System is a standalone cabinet digital X-ray imaging system designed to provide rapid verification that the correct tissue has been excised.

Performing the verification directly in the same procedure room 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 BioVision(Plus) Digital Specimen Radiography (DSR) System employs the use of Faxitron Bioptics Vision image acquisition software. The Vision software handles the digital X-ray image acquisition, calibration, image display, image analysis and manipulation, patient database, image archiving, and transmittal.

The provided text describes the BioVision(Plus) Digital Specimen Radiography (DSR) System and its comparison to a predicate device, the BioVision Digital Specimen Radiography System (K091558). However, it does not describe a study that uses AI or machine learning algorithms, nor does it provide acceptance criteria and performance data in the context of an AI device.

Instead, this document is a 510(k) summary for a medical device that uses X-ray technology. The "study" referenced is a series of non-clinical performance data tests to demonstrate substantial equivalence to a predicate device.

Given that the request asks for details related to AI/ML device studies (e.g., sample size for training data, number of experts, adjudication methods for ground truth, MRMC studies), and the provided text describes an X-ray imaging device without any mention of AI/ML, I cannot fulfill the request as specified.

However, I can extract the information provided regarding the device's technical specifications and the non-clinical testing performed to establish substantial equivalence.

Here's an attempt to answer the questions based only on the information available in the provided text, recognizing that many requested fields regarding AI/ML studies are not applicable:

Acceptance Criteria and Study for BioVision(Plus) Digital Specimen Radiography (DSR) System

The provided document describes the BioVision(Plus) DSR System as an X-ray imaging device, not an AI/ML device. Therefore, many of the requested fields regarding AI/ML study design are not applicable or not present in the text. The "acceptance criteria" here refer to performance standards and substantial equivalence to a predicate device, rather than diagnostic performance metrics of an AI algorithm.

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

The document does not explicitly state "acceptance criteria" in the format of defined thresholds. Instead, it details that the device was tested to perform "as well as" the predicate device and to comply with specific regulations and standards.

2. Sample size used for the test set and the data provenance

The document describes non-clinical performance and validation testing, not a clinical study with a "test set" of patient data in the typical sense for AI/ML.

• Sample size for test set: Not applicable for a non-clinical device performance test. The testing involved phantoms (line pair gauge, America College Radiology phantom, Small Field Low Contrast Phantom) and engineering validation.
• Data provenance: Not applicable. The testing was non-clinical, involving device performance measurements rather than patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. Ground truth, in the context of an AI/ML diagnostic device, involves expert interpretation of patient data or pathology. This document describes performance testing of an X-ray generator and imaging system using phantoms and engineering methods.

4. Adjudication method for the test set

Not applicable. There was no clinical "test set" requiring expert adjudication.

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. This device is a standalone X-ray imaging system, not an AI-assisted diagnostic tool.

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

The device itself is a "standalone" X-ray imaging system. The performance testing was of the system's ability to generate and capture images to a specified quality, without human-in-the-loop performance assessment related to diagnostic accuracy. The system's "algorithm" here refers to its internal software for image acquisition, calibration, display, and manipulation, not an AI algorithm for diagnosis.

7. The type of ground truth used

For the non-clinical performance tests, the "ground truth" was established by:

• Known physical properties of phantoms: For spatial resolution (line pair gauge) and contrast resolution (Small Field Low Contrast Phantom).
• Engineering specifications and regulatory standards: For radiation safety (21 CFR 1020.40) and electrical safety (UL 61010-1).
• Design specifications and verification/validation results: For software functionality and hazard mitigation.

8. The sample size for the training set

Not applicable. This is not an AI/ML device, so there is no "training set."

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

Not applicable. There is no training set for an AI/ML algorithm.

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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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