(80 days)
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.
§ 892.1680 Stationary x-ray system.
(a)
Identification. A stationary x-ray system is a permanently installed diagnostic system intended to generate and control x-rays for examination of various anatomical regions. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II (special controls). A radiographic contrast tray or radiology diagnostic kit intended for use with a stationary x-ray system only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.