Search Results

IVUS Enhancer is a software product intended to be used to review and analyze DICOM images, primarily intravascular ultrasound (IVUS) images. IVUS Enhancer is intended to help qualified medical professionals review DICOM images by adjusting image properties, making simple measurements, and adding annotations for easy export to other applications. These features aid in postprocedure analysis after the placement of interventional devices.

IVUS Enhancer is a software product that provides capabilities for viewing and interacting with DICOM data from Intravascular Ultrasound (IVUS) studies from all vendors, other DICOM medical image data, and INDEC echoPlaque IMG/BMG IVUS files. IVUS Enhancer's main functionality includes viewing and playback of medical images and ancillary files, minor image analysis including some measurements, and the ability to resave image cross-sections and animations to be used in future presentations.

Here's an analysis of the provided text regarding the acceptance criteria and study for the IVUS Enhancer:

It is important to note that the provided text is a 510(k) summary for the IVUS Enhancer, which is a software product for viewing and interacting with DICOM data, performing minor image analysis and measurements, and exporting images. This type of device, being a Picture Archiving and Communication System (PACS) and an "enhancer" for existing images, primarily focuses on functional equivalence and safety rather than demonstrating improved diagnostic accuracy or clinical outcomes through rigorous clinical trials with specific acceptance criteria in the same way a novel diagnostic or therapeutic device would.

Therefore, many of the typical study elements for demonstrating performance (like specific acceptance criteria for diagnostic accuracy, MRMC studies, or large-scale ground truthing) are not present or explicitly stated in a 510(k) for this kind of software. Instead, the focus is on verification and validation activities to ensure the software performs its stated functions correctly and safely, and is substantially equivalent to predicate devices.

Based on the provided text, here's what can be extracted and inferred:

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

The document does not explicitly state quantitative acceptance criteria for device performance in the same way a diagnostic algorithm would (e.g., sensitivity, specificity thresholds). Instead, the "performance" is demonstrated by its intended use and functional capabilities being substantially equivalent to predicate devices.

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

The 510(k) summary does not provide details about a specific "test set" sample size or data provenance for performance evaluation. For software systems like this, testing usually involves verification and validation of features against functional specifications, which would use test cases rather than a "test set" of patient data for diagnostic accuracy. Since it's a PACS-like device, the focus is on handling various DICOM files correctly. We can infer that real-world or simulated DICOM data would have been used for validation, but no specifics are given.

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):

No information is provided regarding the number or qualifications of experts for establishing ground truth. For a device focused on viewing and basic measurements, "ground truth" would likely relate to the accuracy of the software's rendering or measurement algorithms, which is typically validated against known inputs or engineering specifications, rather than clinical expert consensus.

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

No adjudication method is mentioned.

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, an MRMC comparative effectiveness study was not done. This type of study is typically performed for AI-powered diagnostic aids, not for software that primarily provides viewing, measurement, and export functionalities like the IVUS Enhancer. The device is intended to "help qualified medical professionals" by providing tools, not by making independent diagnostic assessments or providing AI-driven insights that would need to be compared against human performance.

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

The concept of "standalone performance" for this device doesn't directly apply in the way it would for an algorithm making a diagnosis. The IVUS Enhancer is explicitly designed for human-in-the-loop use ("intended to help qualified medical professionals"). Its performance pertains to its ability to correctly display images, perform measurements as specified, and export data, which are functionalities verified through software testing.

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

Given the device's function, the "ground truth" would likely be:

• Known DICOM standards: Ensuring images are displayed and parameters are parsed correctly according to the standard.
• Geometric accuracy: For measurements, the ground truth would be mathematically derived or based on known distances in test images/phantoms.
• Functional specifications: Verification that each feature (e.g., image adjustment, annotation saving, export) performs as designed in the software requirements.
• Comparison to predicate devices: Ensuring that the output and functionality are consistent with the established predicate devices.

8. The sample size for the training set:

Not applicable. The IVUS Enhancer is described as a software product providing viewing and measurement capabilities, not an AI/ML algorithm that requires a "training set" in the traditional sense. It's built on programmed logic and algorithms, not learned from data.

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

Not applicable. (See point 8).

Ask a specific question about this device