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The indications for use statement for the proposed Interventional Workspot software medical device, as presented in the IFU, are as follows:

The Interventional Workspot has the following medical purpose:

• import, export, and store digital clinical images.
• manage the patient information associated with those images.

The Interventional Workspot software medical device is a software platform for hosting the aforementioned currently marketed and predicate software medical devices. It provides the same functionalities (for example, import, export, and data handling) that are required by the aforementioned currently marketed and predicate software medical devices to support the physician with performing interventional procedures.

The interventional workspot is a software platform to host Interventional Tools. It provides common functionalities (e.g. import / export and data handling functions) that are required by the Interventional Tools to support the physician with performing the interventional procedure.

This 510(k) summary describes Philips Interventional Workspot, a software platform intended to host other interventional tools, providing functionalities such as import, export, and data handling of digital clinical images and patient information.

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

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state quantitative acceptance criteria or detailed performance metrics. It indicates that the device underwent non-clinical verification and validation tests.

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

The document does not specify a "test set" in the context of clinical data or patient samples. The testing described is "non-clinical verification and validation tests," implying software-based evaluations rather than studies involving patient data. Therefore, details regarding test set sample size and data provenance (country of origin, retrospective/prospective) are not provided and do not appear to be relevant to the type of testing performed for this device.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. As the testing was non-clinical verification and validation of software functionalities, there was no "ground truth" related to medical diagnoses or human interpretation that would require expert adjudication.

4. Adjudication Method

Not applicable. No expert adjudication process is described for non-clinical software testing.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. The document does not describe a multi-reader, multi-case comparative effectiveness study involving human readers or the effect size of AI assistance. This device is a software platform, not an AI-powered diagnostic tool, so such a study would not typically be conducted.

6. Standalone (Algorithm Only) Performance Study

Yes, in the sense that the "Summary of testing" details non-clinical verification and validation tests indicating the algorithm/software's standalone performance in meeting its functional requirements and regulatory standards. However, it's not a standalone clinical performance study as might be conducted for a diagnostic algorithm. The statement "The results of these tests demonstrate that Interventional Workspot met the acceptance criteria" refers to the software's performance in isolation from clinical application.

7. Type of Ground Truth Used

The "ground truth" for the non-clinical verification and validation tests would be the pre-defined requirement specifications and expected behavior of the software functionalities (e.g., successful import/export, correct data handling, DICOM compliance). It does not involve expert consensus, pathology, or outcomes data, as these are related to clinical efficacy or diagnostic accuracy, which are not the focus of this device's testing.

8. Sample Size for the Training Set

Not applicable. This device is described as a software platform providing functionalities like import, export, and data handling. It is not an AI/ML algorithm that requires a "training set" of data in the conventional sense to learn patterns or make predictions. Its development would involve software engineering and testing against functional specifications.

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

Not applicable, as there is no "training set" for this type of software platform. The "ground truth" for its development and testing would be derived from its design specifications and compliance with relevant industry standards (e.g., DICOM for image handling).

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The Innova systems are indicated for use in generating fluoroscopic images of human anatomy for vascular angiography, diagnostic and interventional procedures, and optionally, rotational imaging procedures. They are also indicated for generating fluoroscopic images of human anatomy for cardiology, diagnostic, and interventional procedures. They are intended to replace fluoroscopic images obtained through image intensifier technology. These devices are not intended for mammography applications.

The Innova 4100 IQ, 3100 IQ, 2100 IQ Systems are modified with an optional software feature called StentViz. The StentViz feature enhances the visibility of stents in the x-ray images produced by the Innova systems. Specifically, StentViz provides an enhanced static image of the stent that is derived from the video image sequence as recorded during fluoroscopic guidance. It does not provide real-time guidance.

The information provided focuses on demonstrating substantial equivalence to predicate devices rather than establishing specific acceptance criteria and detailed performance studies for the StentViz feature itself. Therefore, many of the requested details about acceptance criteria, sample sizes, expert involvement, and ground truth establishment are not explicitly stated in the provided text.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly present a table of acceptance criteria with numerical targets. Instead, it relies on a qualitative comparison to predicate devices and general statements about image enhancement.

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

• Sample Size: "Bench tests were performed based on a library of clinical images." The exact number of images in this "library" is not specified.
• Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective. It only mentions "clinical images."

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

• This information is not provided in the document. The evaluation of "enhancement of stent visibility" appears to have been part of bench testing, but who performed these assessments and their qualifications are not mentioned.

4. Adjudication method for the test set:

• This information is not provided.

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:

• A MRMC comparative effectiveness study is not explicitly mentioned. The evaluation described is "bench tests" to assess enhancement and perform comparisons. The document does not discuss human reader performance or improvement with AI assistance (StentViz).

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

• The evaluation was a "bench test" comparing the StentViz feature to the Innova without StentViz and to a predicate device's feature (StentOp). While human eyes would ultimately interpret the enhanced images, the description of "bench tests... to assess the enhancement of stent visibility" and compare performance suggests an evaluation of the algorithm's output (the enhanced image) itself, making it akin to a standalone performance assessment of the image enhancement capability. However, it's not explicitly stated as "algorithm only without human-in-the-loop performance" in the strict sense of a diagnostic task.

7. The type of ground truth used:

• The document mentions "a library of clinical images" being used for bench tests to assess "enhancement of stent visibility." The "ground truth" for assessing this enhancement is not explicitly defined. It likely relied on a subjective or objective assessment of image clarity and stent outline on the enhanced images compared to unenhanced images or images from predicate devices. There is no mention of pathology, or outcomes data being used as ground truth for this enhancement feature.

8. The sample size for the training set:

• The document does not provide information about a specific training set. The StentViz is described as an "optional software feature" that "enhances the visibility of stents." The development process mentioned includes "Risk Analysis, Requirements Reviews, Design Reviews, Testing on unit level (Module verification), Integration testing (System verification), Final acceptance testing (Validation), Performance testing (Verification), Safety testing (Verification)." This suggests a standard software development and testing cycle rather than a machine learning model that would typically have a distinct "training set."

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

• As no specific training set for a machine learning model is mentioned, this information is not applicable/provided. The feature development would have followed established engineering principles for image processing, not a typical machine learning training paradigm with a labeled ground truth dataset.

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