(34 days)
The Philips Medical Systems' Advanced Diffusion Analysis (ADA) application is a post processing software application to be used by trained professionals including but not limited to physicians and medical technicians. The Philips Medical Systems' Advanced Diffusion Analysis (ADA) application can be used to perform image viewing, process and analysis of MRI Diffusion Weighted Images (DWI).
Philips Medical Systems' Advanced Diffusion Analysis (ADA) application is a post-processing software to be used as an advanced visualization application of diffusion MRI medical images. The ADA application is intended to perform image viewing, process and analysis of MRI Diffusion Weighted Images (DWI).
The ADA application can display images acquired at different b-values, where the b-value is a factor that reflects the strength and timing of the gradients used to generate Diffusion-Weighted Images. The ADA application provides advanced supportive analysis and visualization tools of diffusion MRI images and parametric maps, which can be used by the physician for further analysis.
The physician retains the ultimate responsibility for making the final diagnosis.
Key Features:
- Support visualization and processing of isotropic diffusion-weighted MRI data.
- Calculate and display a computed Diffusion Weighted Image (cDWI) at a b-value of choice.
- Support input image registration in a pre-processing step.
- Present a default analysis model based on the available original DWI images and provide a selection of alternative available models.
- Provide diffusion analysis models, as well as parametric maps of Perfusion fraction (f), Pseudo Diffusion coefficient (D*), Diffusion coefficient (D) and Kurtosis (K).
- Provide a 'Goodness of fit' map, 'Goodness of fit' value and fitted curve showing the fitting quality of the selected model.
- Display parameter values from user defined ROI's (Regions of Interest).
- Display the ROI results in tabular and graphical formats.
- Support export of the parametric maps as grayscale or RGB images for visualization in other viewers or PACS systems.
The provided text does not contain detailed information about specific acceptance criteria, reported device performance metrics (e.g., sensitivity, specificity, accuracy), sample sizes for test or training sets, data provenance, expert ground truth establishment, or adjudication methods for the "Advanced Diffusion Analysis (ADA) application."
The FDA 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (Olea Sphere V3.0, K152602) rather than presenting a detailed study proving the ADA application meets specific performance acceptance criteria.
However, based on the provided text, here's what can be extracted and inferred:
1. Table of Acceptance Criteria and Reported Device Performance:
The document states: "Verification and Validation tests have been performed to address intended use, the technological characteristics claims, requirement specifications and the risk management results." and "The test results in this 510(k) premarket notification demonstrate that Advanced Diffusion Analysis (ADA) Complies with the aforementioned international and FDA-recognized consensus standards and FDA guidance document, and Meets the acceptance criteria and is adequate for its intended use and specifications."
This indicates that acceptance criteria were established and the device met them. However, the specific metrics and their corresponding values (e.g., accuracy percentages, error rates, etc.) are not detailed in this document. The "reported device performance" is a general statement of compliance rather than specific quantitative results.
| Acceptance Criteria (Inferred from general statements) | Reported Device Performance (Inferred from general statements) |
|---|---|
| Compliance with ISO 14971, IEC 62304, FDA Guidance for Premarket Submissions for Software, NEMA PS 3.1-3.20 | Complies with all aforementioned standards and guidance documents. |
| Adherence to intended use | Adequate for its intended use. |
| Fulfillment of technological characteristics claims | Meets technological characteristics claims. |
| Satisfaction of requirement specifications | Meets requirement specifications. |
| Mitigation of identified risks | Risk management results addressed (device is safe and effective and introduces no new safety issues). |
| All defined functionality requirements are met. | All defined functionality requirements are met. |
| All performance claims are met. | All performance claims are met. |
2. Sample size used for the test set and the data provenance:
- Sample Size for Test Set: Not specified.
- Data Provenance: Not specified. It can be inferred that the data used for verification and validation would be MRI Diffusion Weighted Images (DWI), but details on origin (e.g., country, specific institutions) or whether it was retrospective/prospective are not provided.
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 general statement is that the application is "to be used by trained professionals including but not limited to physicians and medical technicians." This refers to the end-users, not necessarily experts establishing ground truth for testing.
4. Adjudication method for the test set:
- This information is not provided in the document.
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 or reported. The document explicitly states: "The subject of this premarket submission, Advanced Diffusion Analysis (ADA) application did not require clinical studies to support equivalence." This indicates that studies involving human readers, with or without AI assistance, were not part of this 510(k) submission. The focus was on demonstrating substantial equivalence to a predicate device.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
- The document implies that standalone performance was evaluated during the "Verification and Validation (V&V) activities." The detailed results of this standalone performance, in terms of specific metrics, are not provided, only a general statement that it "meets all defined functionality requirements and performance claims." The device is a "post-processing software application," suggesting an algorithm-only function that generates parametric maps and images for trained professionals to interpret.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- This information is not explicitly stated in the document. Given that the device analyzes Diffusion Weighted Images and outputs parametric maps, the "ground truth" during V&V would likely involve comparisons against established quantitative methods or expert review of the generated maps, but the specifics are absent.
8. The sample size for the training set:
- This information is not provided in the document. The document describes V&V activities, which typically involve testing, not training. If machine learning was used implicitly, no details on training data are given.
9. How the ground truth for the training set was established:
- This information is not provided in the document, as details on a training set itself are absent.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).