(246 days)
GenIQ is an automated post-processing software option that is indicated for use on dynamic magnetic resonance imaging data sets to generate parametric image intensity variations over time. This dynamic change in signal intensity is used to calculate functional parameters related to tissue flow and leakage of the contrast agent from the intravascular to the extracellular space.
GenIQ provides information that when interpreted by a trained physician, can be useful for assessing tissue vascular properties.
GenIQ is a software application used for the pharmacokinetic analysis of Dynamic Contrast Enhanced (DCE) MRI data sets. The application is used to perform a General Kinetic Model (GKM)-based pharmacokinetic modeling of DCE-MRI data. The goal of GenIQ is to extract functional parameters describing tissue vascular properties such as forward and backward transfer constants, plasma volume, and volume of extra-cellular space.
Acceptance Criteria and Study for GenIQ
The provided document describes the GenIQ, an automated post-processing software option for dynamic Magnetic Resonance Imaging (MRI) data sets. It calculates functional parameters related to tissue flow and contrast agent leakage.
1. Acceptance Criteria and Reported Device Performance
The document does not explicitly present a table of acceptance criteria with corresponding performance metrics like sensitivity, specificity, accuracy, or AUC. Instead, it states that "Simulated use testing was performed on digital phantom data referenced by Quantitative Imaging Biomarkers Alliance (QIBA). This validation demonstrated good implementation of the General Kinetic Model."
This implies the acceptance criterion for the phantom study was the accurate implementation of the General Kinetic Model (GKM) as validated against QIBA reference data. The reported performance is that the "validation demonstrated good implementation" of this model.
For clinical data, the document states, "anonymized MR contrast-enhanced images were used as clinical datasets to validate the GenIQ application." Again, specific performance metrics against an acceptance criterion are not detailed, but the overall conclusion is that "GE Healthcare considers the GenIQ application to be as safe, as effective, and performance is substantially equivalent to the predicate device." This suggests the clinical validation aimed to demonstrate performance comparable to its predicate (Philips MR Permeability Software - K130278), which would inherently imply meeting certain effectiveness and safety standards.
Table of Acceptance Criteria and Reported Device Performance (Inferred):
| Acceptance Criteria Category | Specific Metric/Target | Reported Device Performance |
|---|---|---|
| Phantom Data Validation | Accurate implementation of the General Kinetic Model (GKM) as referenced by QIBA. | "Demonstrated good implementation of the General Kinetic Model." |
| Clinical Data Validation | Performance comparable to the predicate device (Philips MR Permeability Software - K130278) in terms of efficacy and safety for assessing tissue vascular properties. | "Considered to be as safe, as effective, and performance is substantially equivalent to the predicate device." |
2. Sample Size and Data Provenance for the Test Set
- Sample Size for Test Set:
- Digital Phantom Data: Not specified.
- Clinical Datasets: Not specified, only described as "anonymized MR contrast-enhanced images."
- Data Provenance: The document does not explicitly state the country of origin.
- Digital Phantom Data: Referenced by the Quantitative Imaging Biomarkers Alliance (QIBA).
- Clinical Data: Described as "anonymized MR contrast-enhanced images." The study is likely retrospective as it used existing "anonymized" images.
3. Number of Experts and Qualifications for Ground Truth (Test Set)
This information is not provided in the document. For the digital phantom data, the ground truth is inherently defined by the QIBA reference data and the mathematical model itself. For the clinical datasets, the method of establishing ground truth (e.g., expert consensus, pathology) and the number/qualifications of experts are not described.
4. Adjudication Method (Test Set)
This information is not provided in the document.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
An MRMC comparative effectiveness study is not explicitly mentioned. The validation focused on the software's ability to implement the GKM and its equivalence to a predicate device. The document states, "GenIQ provides information that when interpreted by a trained physician," implying human-in-the-loop, but there's no study comparing human readers with and without AI assistance to quantify an effect size.
6. Standalone Performance
The evaluation primarily describes the "GenIQ application" as an automated post-processing software option, suggesting a focus on its standalone (algorithm-only) performance in calculating pharmacokinetic parameters from DCE-MRI data. While the output is "interpreted by a trained physician," the validation described (GKM implementation on phantom data, and validation on clinical datasets) focuses on the algorithm's accuracy in producing these parameters.
7. Type of Ground Truth Used
- Digital Phantom Data: The ground truth was based on the Quantitative Imaging Biomarkers Alliance (QIBA) reference data and the theoretical correctness of the General Kinetic Model (GKM). These are essentially simulated/known data sets designed to test the mathematical implementation.
- Clinical Datasets: The document does not specify the type of ground truth used for the clinical validation. It only states that images were used "to validate the GenIQ application." This could imply comparison to a reference standard established by expert consensus, other imaging modalities, or clinical outcomes, but it is not detailed.
8. Sample Size for the Training Set
This information is not provided in the document. The document describes testing and validation, but not the development or training of the software.
9. How Ground Truth for the Training Set was Established
This information is not provided in the document as the training set details 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).