(161 days)
ZedView is intended to be used to assist qualified medical professionals to perform fast and effective pre-operative planning for various surgical procedures related to hip and knee by using 2D image data. The software is basically intended to be standalone, however some part of the software provides features for communicating with PACS servers to acquire the CT data of various patients or to upload planned projects, images or reports to the servers.
ZedView is indicated for pre-operative planning for various surgical procedures related to hip and knee, such as artificial joint replacement (3D templating of implants) and osteotomy.
ZedView is a software package that provides computer-assisted 3D planning and evaluations using 2D image data in DICOM or other formats, for various pre-operative hip and knee surgical procedures. The software is composed of various modules as shown in Figure 1, below.
ZedView is intended to be used to assist qualified medical professionals to perform fast and effective pre-operative planning for various surgical procedures related to hip and knee by using 2D image data. The software is basically intended to be standalone, however some part of the software provides features for communicating with PACS servers to acquire the CT data of various patients or to upload planned projects, images or reports to the servers.
The software primarily provides import and storage of CT images of various patients in DICOM or other formats. Also, it provides a means of 3D templating of implants and positioning of fixation devices by calculating surgical parameters in simulated environments and performing 3D measurements on each pre-operative patient data, using 2D image viewing and manipulations, 3D visualizations and various MPR (Multi-Planar Reconstruction) functions.
The software also provides separate modules that support pre-operative planning of hip and knee arthroplasty for 2D digital X-rav images obtained with the EOS imaging system by providing quasi-3D templating, 3D measurement, etc. Besides the functional modules for artificial joint replacement surgeries, the software also provides a module that incorporates planning and evaluations for osteotomy (Curved Periacetabular Osteotomy etc.).
The provided text does not contain specific acceptance criteria or a detailed study proving the device meets those criteria. Instead, it describes a 510(k) summary for a medical image management software called ZedView, focusing on its substantial equivalence to predicate devices (Meridian Technique Ltd. Orthoview™).
However, I can extract the information available from the document regarding the device's performance claims and the nature of the evaluation.
Here's an analysis based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state numerical acceptance criteria, nor does it provide quantitative performance metrics for ZedView. The performance section broadly claims that the software "meets its requirements for intended use and its performance requirements" and is "as safe, as effective, and performs as well as or better than the predicates."
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Meet requirements for intended use | Verification and validation results confirm ZedView meets its requirements for intended use. |
| Meet performance requirements | Verification and validation results confirm ZedView meets its performance requirements. |
| As safe as predicates | Non-clinical tests confirm ZedView is as safe as the predicates. |
| As effective as predicates | Non-clinical tests confirm ZedView is as effective as the predicates. |
| Performs as well as or better than predicates | Non-clinical tests confirm ZedView performs as well as or better than the predicates. |
2. Sample Size Used for the Test Set and Data Provenance
The text states: "The Lexi ZedView Software was fully tested, verified and validated by Lexi as part of its own internal design control requirements using the test image data or real-life patient data."
- Test Set Sample Size: Not specified.
- Data Provenance: The text mentions "test image data or real-life patient data," but does not specify the country of origin or whether it was retrospective or prospective.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
This information is not provided in the document. The text does not elaborate on how ground truth was established for any test data used.
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
- MRMC Study: No, an MRMC comparative effectiveness study is not mentioned in the document. The evaluation described is internal verification and validation against predicate devices, not a study of human reader improvement with AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
Yes, the device is explicitly described as "basically intended to be standalone." The performance evaluation mentioned (internal design control requirements, verification, and validation) would inherently assess its standalone functionality, comparing it to its requirements and predicates, rather than its impact on human performance in a clinical setting.
7. The Type of Ground Truth Used
The type of ground truth used is not explicitly stated. The document mentions "test image data or real-life patient data" but does not detail how the definitive truth for these cases was established (e.g., pathology, clinical outcomes, expert consensus). Given the device's function as a pre-operative planning tool, the ground truth would likely relate to the accuracy of 3D reconstructions, measurements, and implant templating against known anatomical or surgical parameters, potentially derived from expert consensus or cadaveric studies, but none of this is detailed.
8. The Sample Size for the Training Set
This information is not provided. The document describes a traditional software verification and validation process, not a machine learning model that typically involves distinct training and test sets. While the software uses "real-life patient data" for testing, it's not clear if there's a "training set" in the context of an AI/ML algorithm.
9. How the Ground Truth for the Training Set Was Established
Since a "training set" (in the context of AI/ML) is not mentioned, and the process of establishing ground truth for any data used is not detailed, this information is not provided.
§ 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).