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The purpose of Visaris Vision® is to acquire, store, communicate, display and process medical X-ray images. These radiographic systems are intended for use by a qualified/trained physician or both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography, interventional, or fluoroscopy use.

Visaris Vision combines our Visaris Avanse® cleared PACS software (K150725) with all the other components required to make a complete digital diagnostic X-ray system, including tube stands, tube heads, collimators, generators, tables, and (already cleared) digital X-ray panels. Visaris Avanse®, the software component, is a digital radiography imaging, control and management software (for imaging consoles) that works with DR flat panel detector technology. Visaris Avanse® PACS provides functionality for digital radiography examinations from patient search and entry, generator control, image acquisition and processing to DICOM data archiving and export. Visaris Avanse® PACS can also include a number of Digital Radiology modules such as network DICOM archive (PACS module), DICOM modality worklist module and diagnostic workstation software to turn it into digital radiology department on a PC for small clinics. Visaris Avanse ® is autonomous software and involves no hardware. It runs under the MS Windows XP/7/8 operating system on any hardware platform meeting the minimum system requirements. These models are available: Vision C: A universal digital system employing an Overhead Tube Crane, a table, and a wall stand. Vision U: A universal U-Arm system Vision V: A floor mounted tube system Vision X: A universal straight Arm system All of these come with the previously cleared Visaris 360, the integrated digital radiology workflow system. (Visaris Avance, K150725)

This document describes a 510(k) premarket notification for the "Visaris Vision" digital X-ray system. This system is a collection of components, including X-ray tubes, generators, collimators, tables, and digital X-ray panels, controlled by the Visaris Avanse® PACS software. The submission seeks to demonstrate substantial equivalence to a predicate device, the Siemens Multix Fusion (K121513).

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

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

The provided document does not explicitly present a dedicated table of "acceptance criteria" for the Visaris Vision system in the way one might expect for a specific performance metric (e.g., sensitivity, specificity for a diagnostic algorithm). Instead, the acceptance criteria are implicit in demonstrating substantial equivalence to the predicate device and compliance with recognized standards.

The "Comparison Table" in Section 4 (Page 5) compares the technological characteristics of the Visaris Vision with the predicate device (Siemens Multix Fusion K12113). While not framed as "acceptance criteria," these represent the benchmarks the device must meet to be considered substantially equivalent.

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 a "clinical evaluation" was performed. However, it does not specify a distinct "test set" with a given sample size for this evaluation in terms of patient numbers or image counts, nor does it provide details on data provenance (country of origin, retrospective/prospective). The clinical evaluation's primary objective seems to be system verification and risk assessment rather than a quantitative performance evaluation of a specific diagnostic output.

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)

The document does not mention the use of experts to establish ground truth for a test set. The clinical evaluation focused on "verification in the normal working conditions" and "determination of all adverse effects," implying system functionality and safety checks rather than a diagnostic accuracy study requiring expert-derived ground truth.

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

No adjudication method is mentioned as a "test set" for diagnostic performance was not described.

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 such MRMC comparative effectiveness study is mentioned. This device is an X-ray system, not an AI-powered diagnostic algorithm designed to assist human readers. The software component, Visaris Avanse®, is described as "a digital radiography imaging, control and management software... provides functionality for digital radiography examinations from patient search and entry, generator control, image acquisition and processing to DICOM data archiving and export." This suggests image acquisition and management, not AI-driven interpretation assistance.

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

A "standalone" performance for an algorithm is not applicable here as the device is an X-ray imaging system, not a diagnostic algorithm that provides standalone interpretations. The system's "performance" is tied to its ability to acquire, store, and process medical X-ray images, as demonstrated by non-clinical testing against standards and successful integration of components.

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

Given that no diagnostic accuracy study is described, there's no mention of ground truth established through expert consensus, pathology, or outcomes data. The "clinical evaluation" appears to focus on verifying the device's functional specifications and safety in a real-world setting.

8. The sample size for the training set

This information is not provided and is not applicable in the context of this 510(k) submission, which is for an X-ray imaging system, not a machine learning or AI algorithm requiring a training set in the typical sense. The software component (Visaris Avanse) was "used unmodified from our clearance obtained in K150725," implying that any development/training specific to that software would have been addressed in its prior submission.

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

As there's no described training set for a diagnostic algorithm, this information is not provided.

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The purpose of Visaris Avanse® is to acquire, store, communicate, display and process medical X-ray images. It offers features (e.g. window leveling, zoom, measurements, annotations etc.) routinely used by medical professionals, such as radiologists and radiographers. Visaris Avanse supports printing to DICOM compatible printers. Within a network environment Visaris Avanse may provide other modalities with a DICOM worklist and a DICOM worklist service. Images and worklists can be sent and received using the DICOM protocol. Visaris Avanse has a modular system architecture. It consists of the basic application, processing and viewing as well as a number of other modules for image and worklist management, archiving, search and display. Beside the basic functionality Visaris Avanse also provides a user interface for generator control and image acquisition of medical images DR detectors.

Visaris Avanse® is a digital radiography imaging, control and management software (for imaging consoles) that works with DR flat panel detectors technology. Visaris Avanse® PACS provides functionality for digital radiography examinations from patient search and entry, generator control, image acquisition and processing to DICOM data archiving and export. Visaris Avanse® PACS can also include a number of Digital Radiology modules such as network DICOM archive (PACS module). DICOM modality worklist module and diagnostic workstation software to turn it into digital radiology department on a PC for small clinics. Visaris Avanse ® is autonomous software and involves no hardware. It runs under the MS Windows XP/7/8 operating system on any hardware platform meeting the minimum system requirements.

This document is a 510(k) premarket notification for the "Visaris Avanse®" device, which is a software for digital radiography imaging, control, and management. It focuses on establishing substantial equivalence to a predicate device, not on proving meeting acceptance criteria through a specific study with quantitative performance metrics.

Therefore, many of the requested elements for describing "acceptance criteria and the study that proves the device meets the acceptance criteria" cannot be fully extracted or are not directly applicable in the typical sense of AI/CADe device performance studies.

Here's a breakdown of what can be inferred and what is not available:

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

The document does not provide a table of explicit quantitative "acceptance criteria" and "reported device performance" in the context of clinical efficacy or diagnostic accuracy. Instead, the "acceptance criteria" for this submission are demonstrated through a comparison of technological characteristics with a predicate device, and verification/validation testing against in-house criteria.

• Acceptance Criteria (Implicitly based on Predicate Equivalence and Functional Testing):
  • Maintain same essential indications for use.
  • Utilize same kind of platform (Windows PC).
  • Perform similar software validation and panel testing.
  • Be DICOM compatible.
  • Handle JPEG images.
  • Meet all in-house criteria demonstrated by Verification and Validation plan.
  • Produce images of excellent diagnostic quality.
• Reported Device Performance (Implicit, Qualitative, and Comparative):
  • "The Visaris Avanse® has the same essential indications for use as the predicate and uses the same kind of platform (Windows PC) to perform its functions." (Substantial Equivalence Discussion)
  • "The same kind of software validation and panel testing has been performed." (Substantial Equivalence Discussion)
  • "It is DICOM compatible like the predicate and handles jpeq images like the predicate." (Substantial Equivalence Discussion)
  • "The complete Visaris Avanse® PACS system configuration has been assessed and tested at the factory and has passed all in-house criteria." (Non clinical testing)
  • "Validation testing indicate that as required by risk analysis, designated individuals performed all verification and validation activities and that the result demonstrated that the predetermine acceptance criteria were met." (Non clinical testing)
  • "The images were evaluated by a board certified radiologist and were found to be of excellent diagnostic quality." (Clinical testing)

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

• Sample Size for Test Set: Not explicitly stated. The "Clinical testing" section mentions "clinical images were obtained using each of the declared compatible digital panels." It does not specify the number of images or patients.
• Data Provenance: Not specified. It only states "clinical images were obtained." Whether this was retrospective or prospective, or the country of origin is not mentioned.

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)

• Number of Experts: "A board certified radiologist" (singular) was used.
• Qualifications: "Board certified radiologist." Specific experience (e.g., 10 years) is not provided.

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

• Adjudication Method: Not specified. Given there was only "a board certified radiologist," an adjudication method involving multiple readers is unlikely. This appears to be a single-reader evaluation.

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

• MRMC Comparative Effectiveness Study: No, an MRMC comparative effectiveness study was not done. This submission is for a software device that acquires, stores, communicates, displays, and processes X-ray images, and provides a user interface for generator control. It is not an AI/CADe device designed to assist human readers in diagnosis or detection, so a study comparing human readers with and without AI assistance is not relevant to this device's function as described.

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

• Standalone Study: No, this is not an AI/CADe algorithm for diagnosis. The "device" is the software itself, which facilitates image management and viewing. Its "performance" is assessed functionally (e.g., compatibility, image quality), not as a diagnostic algorithm. The clinical testing verifies the diagnostic quality of the images produced by the system, not the diagnostic performance of an algorithm.

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

• Type of Ground Truth: The "ground truth" for the clinical testing appears to be the qualitative assessment by a board-certified radiologist that the images were "of excellent diagnostic quality." There's no mention of pathology, outcomes data, or a formal consensus process.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable/not provided. This device is not an AI/ML algorithm that requires a training set in the conventional sense. It's a general-purpose image acquisition, processing, and management software.

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

• How Ground Truth for Training Set was Established: Not applicable, as there is no mention of an AI/ML training set.

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