(142 days)
Medical purpose EP navigator is intended to provide navigation support for intra-cardiac instruments, such as catheters and guidewires, during the interventional treatment of heart rhythm disorders, by overlaying acquired and segmented 3D anatomical image data over live fluoroscopic X-ray images of the same anatomy.
EP navigator is intended to enable users to segment previously acquired 3D CT or other datasets and overlay and register these 3D segmented data sets with live fluoroscopy X-ray images of the same anatomy in order to support catheter/device navigation. The 3D segmented data set can be displayed with a color map annotation received from an external source.
EP navigator image software processing algorithms are executed on a PC based hardware platform, which can perform the following functions:
- segment previously acquired DICOM 3D CT or other image data.(the acquisition of . the image data from a rotational angiogram is known as 3D atriography (3D ATG))
- superimpose the segmented 3D CT or other dataset on a live fluoroscopic X-ray image of the same anatomy, obtained on a Philips Allura Xper FD angiography X-ray system,
- . register the segmented 3D CT or other data with live fluoroscopic X-ray images obtained on a Philips Allura Xper FD angiography X-ray system for specified procedures.
- The 3D segmented data set can be displayed with a color map annotation received ● from an external source.
- position visual markers on the 3D volume ●
- visualize the inside of the 3D volume (EndoView) .
- certain buttons on the user interface control EP Logix functions; ●
- visual marker positions are transmitted to EP Logix; .
- color map information is received from EP Logix. .
The provided document is a 510(k) Summary for the Philips EP navigator device. It describes the device, its intended use, indications for use, and a summary of testing to demonstrate substantial equivalence to predicate devices. However, the document does not contain the specific details required to fully address your request regarding acceptance criteria and the comprehensive study that proves the device meets those criteria.
Here's what can be extracted and what is missing:
1. Table of Acceptance Criteria and Reported Device Performance:
The document mentions "EP navigator 3 complies with standards as detailed in annex 009 of this premarket submission" and "Non-clinical verification and validation tests were performed relative to the requirement specifications and risk management results". It also states that a "Clinical evaluation was performed to show safety and effectiveness to of EP-Navigator in the intended clinical environment."
However, the document does not provide a table specifying the acceptance criteria (e.g., accuracy metrics, specific performance thresholds) nor does it report detailed device performance against those criteria. It merely states that the device "complies" and that "clinical evaluation" showed "safety and effectiveness."
2. Sample Size Used for the Test Set and Data Provenance:
The document makes a general statement: "Corresponding clinical evaluation report and test results are included in this submission."
However, it does not specify the sample size for any test set or the data provenance (e.g., country of origin, retrospective/prospective nature of data).
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
The document mentions a "Clinical evaluation" but does not specify the number of experts, their qualifications, or how ground truth was established for any test set.
4. Adjudication Method for the Test Set:
No information on adjudication methods (e.g., 2+1, 3+1, none) is provided.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
The document does not mention any MRMC study or the effect size of human readers improving with AI assistance. It focuses on the device's standalone capabilities and its intended use for navigation support.
6. Standalone (Algorithm Only) Performance:
The document states that "EP navigator image software processing algorithms are executed on a PC based hardware platform," and its functions involve "segment previously acquired DICOM 3D CT or other image data," "superimpose the segmented 3D CT or other dataset on a live fluoroscopic X-ray image," and "register the segmented 3D CT or other data with live fluoroscopic X-ray images."
This strongly implies that the algorithm has standalone performance for these image processing and registration tasks. However, the document does not explicitly present a dedicated standalone performance study with specific metrics, but rather refers to "non-clinical verification and validation tests performed relative to the requirement specifications."
7. Type of Ground Truth Used:
While "clinical evaluation" and "non-clinical verification and validation tests" are mentioned, the specific type of ground truth used (e.g., expert consensus, pathology, outcomes data) for validating the device's performance is not detailed.
8. Sample Size for the Training Set:
The document does not provide any information regarding a training set or its sample size. Given that it is an "image software processing algorithm," it likely involves some form of training or calibration, but this is not discussed in the summary.
9. How the Ground Truth for the Training Set Was Established:
As no training set information is provided, there is no description of how ground truth for a training set was established.
In summary:
The 510(k) Summary focuses on demonstrating substantial equivalence to predicate devices based on intended use, technological characteristics, and safety risks. It indicates that clinical evaluation and non-clinical verification/validation were performed, but it lacks the specific quantitative details about acceptance criteria, detailed study designs (test set size, provenance, expert involvement, adjudication), and training set information that you've requested. This level of detail is typically found in the full submission referenced by the summary, not in the summary itself.
§ 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).